The PASCAL Visual Object Classes Challenge 2008 (VOC2008) Development Kit (original) (raw)

Mark Everingham - John Winn

Contents

1 Challenge

The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). There are twenty object classes:

2 Data

The VOC2008 database contains a total of 10,057 annotated images. The data is released in two phases: (i) training and validation data with annotation is released with this development kit; (ii) test data without annotation is released at a later date. After completion of the challenge, annotation for the test data will be released.

2.1 Classification/Detection Image Sets

For the main tasks - classification and detection, there are four sets of images provided:

train:

Training data

val:

Validation data (suggested). The validation data may be used as additional training data (see below).

trainval:

The union of train andval.

test:

Test data. The test set is not provided in the development kit. It will be released in good time before the deadline for submission of results.

Table 1 summarizes the number of objects and images (containing at least one object of a given class) for each class and image set. The data has been split into 50% for training/validation and 50% for testing. The distributions of images and objects by class are approximately equal across the training/validation and test sets.

**Table 1:**Statistics of the main image sets. Object statistics list only the `non-difficult' objects used in the evaluation.

train val trainval test
img obj img obj img obj img obj
Aeroplane 119 159 117 157 236 316 - -
Bicycle 92 133 100 136 192 269 - -
Bird 166 239 139 237 305 476 - -
Boat 111 170 96 166 207 336 - -
Bottle 129 229 114 228 243 457 - -
Bus 48 61 52 68 100 129 - -
Car 243 426 223 414 466 840 - -
Cat 159 186 169 192 328 378 - -
Chair 177 313 174 310 351 623 - -
Cow 37 61 37 69 74 130 - -
Diningtable 53 55 52 55 105 110 - -
Dog 186 238 202 239 388 477 - -
Horse 96 139 102 146 198 285 - -
Motorbike 102 137 102 135 204 272 - -
Person 947 1996 1055 2172 2002 4168 - -
Pottedplant 85 178 95 183 180 361 - -
Sheep 32 67 32 78 64 145 - -
Sofa 69 74 65 77 134 151 - -
Train 78 83 73 83 151 166 - -
Tvmonitor 107 138 108 136 215 274 - -
Total 2113 5082 2227 5281 4340 10363 - -

2.2 Segmentation Taster Image Sets

For the segmentation taster task, corresponding image sets are provided as in the classification/detection tasks. To increase the amount of data, the training and validation image sets include images from the 2007 segmentation taster, indicated by the `2007' prefix. The test set contains only new images, and is a subset of the test set for the main tasks for which pixel-wise segmentations have been prepared. Table 2 summarizes the number of objects and images (containing at least one object of a given class) for each class and image set, for the combined 2007 and 2008 data. In addition to the segmented images for training and validation, participants are free to use the un-segmented training/validation images supplied for the main classification/detection tasks.

**Table 2:**Statistics of the segmentation taster image sets.

train val trainval test
img obj img obj img obj img obj
Aeroplane 32 35 26 32 58 67 - -
Bicycle 27 35 23 33 50 68 - -
Bird 35 44 24 33 59 77 - -
Boat 33 56 27 31 60 87 - -
Bottle 30 55 28 41 58 96 - -
Bus 25 31 27 40 52 71 - -
Car 46 76 34 77 80 153 - -
Cat 30 34 35 39 65 73 - -
Chair 49 109 40 79 89 188 - -
Cow 19 48 25 41 44 89 - -
Diningtable 35 36 32 35 67 71 - -
Dog 26 31 39 51 65 82 - -
Horse 30 38 37 40 67 78 - -
Motorbike 33 37 26 38 59 75 - -
Person 172 299 171 320 343 619 - -
Pottedplant 26 46 34 82 60 128 - -
Sheep 20 49 27 72 47 121 - -
Sofa 27 31 33 41 60 72 - -
Train 27 32 30 34 57 66 - -
Tvmonitor 36 44 33 44 69 88 - -
Total 511 1166 512 1203 1023 2369 - -

2.3 Person Layout Taster Image Sets

For the person layout taster task, corresponding image sets are provided as in the classification/detection tasks. A person is indicated by a bounding box, and each person has been annotated with part layout (head, hands, feet). As in the segmentation taster task, the training and validation image sets include images from the 2007 person layout taster, indicated by the `2007' prefix. The test set contains only new images, and is disjoint from the test set for the main tasks. Table 3summarizes the number of `person' objects annotated with layout for each image set.

**Table 3:**Statistics of the person layout taster image sets. Object statistics list only the `person' objects for which layout information (parts) is present.

train val trainval test
img obj img obj img obj img obj
Person 141 202 104 165 245 367 - -

2.4 Ground Truth Annotation

Objects of the twenty classes listed above are annotated in the ground truth. For each object, the following annotation is present:

2.5 Segmentation Taster Ground Truth

For the segmentation image sets, each image has two corresponding types of ground truth segmentation provided:

2.6 Person Layout Taster Ground Truth

For the person layout taster task, `person' objects are additionally annotated with three `parts':

3 Classification Task

3.1 Task

For each of the twenty object classes predict the presence/absence of at least one object of that class in a test image. The output from your system should be a real-valued confidence of the object's presence so that a precision/recall curve can be drawn. Participants may choose to tackle all, or any subset of object classes, for example ``cars only'' or ``motorbikes and cars''.

3.2 Competitions

Two competitions are defined according to the choice of training data: (i) taken from the VOCtrainval data provided, or (ii) from any source excluding the VOCtest data provided:

No. Task Training data Test data
1 Classification trainval test
2 Classification any but VOC test test

In competition 1, any annotation provided in the VOCtrain andval sets may be used for training, for example bounding boxes or particular views e.g. `frontal' or `left'. Participants are not permitted to perform additional manual annotation of either training or test data. In competition 2, any source of training data may be used_except_ the provided test images. Researchers who have pre-built systems trained on other data are particularly encouraged to participate. The test data includes images from ``flickr'' (www.flickr.com); this source of images may_not_ be used for training. Participants who have acquired images from flickr for training must submit them to the organizers to check for overlap with the test set.

3.3 Submission of Results

A separate text file of results should be generated for each competition (1 or 2) and each class e.g. `car'. Each line should contain a single identifier and the confidence output by the classifier, separated by a space, for example:

comp1_cls_test_car.txt: 2008_000002 0.129824 2008_000005 0.556163 2008_000010 0.227097 2008_000014 0.764145 2008_000016 0.098249

Greater confidence values signify greater confidence that the image contains an object of the class of interest. The example classifier implementation (section 7.2.1) includes code for generating a results file in the required format.

3.4 Evaluation

The classification task will be judged by the precision/recall curve. The principal quantitative measure used will be the average precision (AP). Example code for computing the precision/recall and AP measure is provided in the development kit. Images which contain only objects marked as `difficult' (section 2.4) are currently _ignored_ by the evaluation. The final evaluation may include separate results including such ``difficult'' images, depending on the submitted results. Participants are expected to submit a single set of results per method employed. Participants who have investigated several algorithms may submit one result per method. Changes in algorithm parameters do not constitute a different method - all parameter tuning must be conducted using the training and validation data alone.

4 Detection Task

4.1 Task

For each of the twenty classes predict the bounding boxes of each object of that class in a test image (if any). Each bounding box should be output with an associated real-valued confidence of the detection so that a precision/recall curve can be drawn. Participants may choose to tackle all, or any subset of object classes, for example ``cars only'' or ``motorbikes and cars''.

4.2 Competitions

Two competitions are defined according to the choice of training data: (i) taken from the VOCtrainval data provided, or (ii) from any source excluding the VOCtest data provided:

No. Task Training data Test data
3 Detection trainval test
4 Detection any but VOC test test

In competition 3, any annotation provided in the VOCtrain andval sets may be used for training, for example bounding boxes or particular views e.g. `frontal' or `left'. Participants are not permitted to perform additional manual annotation of either training or test data. In competition 4, any source of training data may be used_except_ the provided test images. Researchers who have pre-built systems trained on other data are particularly encouraged to participate. The test data includes images from ``flickr'' (www.flickr.com); this source of images may_not_ be used for training. Participants who have acquired images from flickr for training must submit them to the organizers to check for overlap with the test set.

4.3 Submission of Results

A separate text file of results should be generated for each competition (3 or 4) and each class e.g. `car'. Each line should be a detection output by the detector in the following format:

<image identifier> <confidence> <left> <top> <right> <bottom>

where (left,top)-(right,bottom) defines the bounding box of the detected object. The top-left pixel in the image has coordinates $(1,1)$. Greater confidence values signify greater confidence that the detection is correct. An example file excerpt is shown below. Note that for the image2008_000016, multiple objects are detected:

comp3_det_test_car.txt: 2008_000014 0.764145 44.182900 49.462700 466.030200 235.963600 2008_000016 0.098249 15.763800 81.605700 486.900500 220.593300 2008_000016 0.098249 200.044400 58.959100 359.902300 99.811700 2008_000016 0.098249 13.543500 47.413900 177.841900 96.703400 2008_000016 0.098249 467.806400 69.616300 500.000000 103.364100 2008_000020 0.112754 91.691600 35.703600 482.859300 305.613800

The example detector implementation (section 7.2.2) includes code for generating a results file in the required format.

4.4 Evaluation

The detection task will be judged by the precision/recall curve. The principal quantitative measure used will be the average precision (AP). Example code for computing the precision/recall and AP measure is provided in the development kit. Detections are considered true or false positives based on the area of overlap with ground truth bounding boxes. To be considered a correct detection, the area of overlap $a_o$ between the predicted bounding box $B_p$ and ground truth bounding box$B_{gt}$ must exceed $50\%$ by the formula:

\begin{displaymath} a_o = \frac{area(B_p \cap B_{gt})}{area(B_p \cup B_{gt})} \end{displaymath} (1)

Example code for computing this overlap measure is provided in the development kit. Multiple detections of the same object in an image are considered false detections e.g. 5 detections of a single object is counted as 1 correct detection and 4 false detections - it is the responsibility of the participant's system to filter multiple detections from its output. Objects marked as `difficult' (section 2.4) are currently_ignored_ by the evaluation. The final evaluation may include separate results including such ``difficult'' images, depending on the submitted results. Participants are expected to submit a single set of results per method employed. Participants who have investigated several algorithms may submit one result per method. Changes in algorithm parameters do not constitute a different method - all parameter tuning must be conducted using the training and validation data alone.

5 Segmentation Taster

5.1 Task

For each test image pixel, predict the class of the object containing that pixel or 'background' if the object does not belong to one of the twenty specified classes. The output from your system should be an indexed image with each pixel index indicating the number of the inferred class (1-20) or zero, indicating background.

5.2 Competitions

A single competition is defined:

No. Task Training data Test data
5 Segmentation trainval test

Any annotation provided in the VOCtrain andval sets may be used for training, for example segmentation, bounding boxes or particular views e.g. `frontal' or `left'. Both the images with and without segmentation provided may be used if desired. Participants are not permitted to perform additional manual annotation of either training or test data.

5.3 Submission of Results

Submission of results should be as collections of PNG format indexed image files, one per test image, with pixel indices from 0 to 20. The example segmenter implementation (section 7.2.3) includes code for generating results in the required format. Along with the submitted image files, participants should also state whether their method used segmentation training data only or both segmentation and bounding box training data.

5.4 Evaluation

The segmentation taster task will be judged by average segmentation accuracy across the twenty classes and the background class. For VOC2008 the segmentation accuracy for a class will be assessed using the intersection/union metric, defined as the number of correctly labelled pixels of that class, divided by the number of pixels labelled with that class in either the ground truth labelling or the inferred labelling. Equivalently, the accuracy is given by the equation,

| $\displaystyle \mathrm{segmentation~accuracy} = \frac{\mathrm{true~positives}}{\mathrm{true~positives} + \mathrm{false~positives} + \mathrm{false~negatives}}$ | | | | | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | |

Code is provided to compute segmentation accuracies for each class, and the overall average accuracy (see section 8.5.2). Participants are expected to submit a single set of results per method employed. Participants who have investigated several algorithms may submit one result per method. Changes in algorithm parameters do not constitute a different method - all parameter tuning must be conducted using the training and validation data alone.

6 Person Layout Taster

6.1 Task

For each `person' object in a test image (their bounding box is provided) predict the presence/absence of parts (head/hands/feet), and the bounding boxes of those parts. Each prediction for a person layout should be output with an associated real-valued confidence of the layout so that a precision/recall curve can be drawn. Multiple estimates of layout may be output for each person, but estimates other than the first correct are treated as false positives as in the detection task. As noted, the bounding box of the person is provided. To be considered a correct estimate of the layout, two conditions must be satisfied: i) correct prediction of parts present/absent; ii) correct prediction of bounding boxes for all parts.

6.2 Competitions

Two competitions are defined according to the choice of training data: (i) taken from the VOCtrainval data provided, or (ii) from any source excluding the VOCtest data provided:

No. Task Training data Test data
6 Layout trainval test
7 Layout any but VOC test test

In competition 6, any annotation provided in the VOCtrain andval sets may be used for training, for example bounding boxes or particular views e.g. `frontal' or `left'. Participants are not permitted to perform additional manual annotation of either training or test data. In competition 7, any source of training data may be used_except_ the provided test images. Researchers who have pre-built systems trained on other data are particularly encouraged to participate. The test data includes images from ``flickr'' (www.flickr.com); this source of images may_not_ be used for training. Participants who have acquired images from flickr for training must submit them to the organizers to check for overlap with the test set.

6.3 Submission of Results

To support the hierarchical (person+parts) nature of this task, an XML format has been adopted for submission of results. A separate XML file of results should be generated for each competition (6 or 7). The overall format should follow:

... layout estimate 1 ... ... layout estimate 2 ...

Each detection is represented by a element. The order of detections is not important. An example detection is shown here:

<layout>
    <image>2008_000200</image>
    <object>3</object>
    <confidence>-8</confidence>
    <part>
        <class>head</class>
        <bndbox>
            <xmin>78</xmin>
            <ymin>82</ymin>
            <xmax>120</xmax>
            <ymax>136</ymax>
        </bndbox>
    </part>
    <part>
        <class>hand</class>
        <bndbox>
            <xmin>41</xmin>
            <ymin>151</ymin>
            <xmax>74</xmax>
            <ymax>190</ymax>
        </bndbox>
    </part>
    <part>
        <class>hand</class>
        <bndbox>
            <xmin>119</xmin>
            <ymin>146</ymin>
            <xmax>147</xmax>
            <ymax>184</ymax>
        </bndbox>
    </part>
</layout>

The element specifies the image identifier. The specifies the index of the object to which the layout relates (the first object in the image has index 1) and should match that provided in the image set files (section 8.1.4). The element specifies the confidence of the layout estimate, used to generate a precision/recall curve as in the detection task. Each element specifies the detection of a particular part of the person i.e. head/hand/foot. If the part is predicted to be absent/invisible, the corresponding element should be omitted. For each part, the element specifies the type of part: head, hand or foot. The element specifies the predicted bounding box for that part; bounding boxes are specified in image co-ordinates and need not be contained in the provided person bounding box. To ease creation of the required XML results file for MATLAB users, a function is included in the development kit to convert MATLAB structures to XML. See the VOCwritexml function (section 8.6.1). The example person layout implementation (section 7.2.4) includes code for generating a results file in the required format.

6.4 Evaluation

The person layout task will be judged by the precision/recall curve. The principal quantitative measure used will be the average precision (AP). Example code for computing the precision/recall and AP measure is provided in the development kit. To be considered a true positive, each layout estimate must satisfy two criteria:

7 Development Kit

The development kit is packaged in a single gzipped tar file containing MATLAB code and (this) documentation. The images, annotation, and lists specifying training/validation sets for the challenge are provided in a separate archive which can be obtained via the VOC web pages [1].

7.1 Installation and Configuration

The simplest installation is achieved by placing the development kit and challenge databases in a single location. After untarring the development kit, download the challenge image database and untar into the same directory, resulting in the following directory structure:

VOCdevkit/                           % development kit
VOCdevkit/VOCcode/                   % VOC utility code
VOCdevkit/results/VOC2008/           % your results on VOC2008
VOCdevkit/results/VOC2007/           % your results on VOC2007
VOCdevkit/local/                     % example code temp dirs
VOCdevkit/VOC2008/ImageSets          % image sets
VOCdevkit/VOC2008/Annotations        % annotation files
VOCdevkit/VOC2008/JPEGImages         % images
VOCdevkit/VOC2008/SegmentationObject % segmentations by object
VOCdevkit/VOC2008/SegmentationClass  % segmentations by class

If you set the current directory in MATLAB to theVOCdevkit directory you should be able to run the example functions:

% change this path to point to your copy of the PASCAL VOC data VOCopts.datadir='/homes/group/VOCdata/';

% change this path to a writable directory for your results VOCopts.resdir='/homes/me/VOCresults/';

% change this path to a writable local directory for the example code VOCopts.localdir='/tmp/';

Note that in developing your own code you need to include theVOCdevkit/VOCcode directory in your MATLAB path, e.g.

addpath /homes/me/code/VOCdevkit/VOCcode

7.2 Example Code

Example implementations are provided for all tasks. The aim of these (minimal) implementations is solely to demonstrate use of the code in the development kit.

7.2.1 Example Classifier Implementation

The file example_classifier.m contains a complete implementation of the classification task. For each VOC object class a simple classifier is trained on thetrain set; the classifier is then applied to theval set and the output saved to a results file in the format required by the challenge; a precision/recall curve is plotted and the `average precision' (AP) measure displayed.

7.2.2 Example Detector Implementation

The file example_detector.m contains a complete implementation of the detection task. For each VOC object class a simple (and not very successful!) detector is trained on thetrain set; the detector is then applied to the val set and the output saved to a results file in the format required by the challenge; a precision/recall curve is plotted and the `average precision' (AP) measure displayed.

7.2.3 Example Segmenter Implementation

An example segmenter is provided which converts detection results into segmentation results, usingcreate_segmentations_from_detections (described below). For example:

example_detector; example_segmenter;

This runs the example detector, converts the detections into segmentations and displays a table of per-class segmentation accuracies, along with an overall average accuracy.

7.2.4 Example Layout Implementation

The file example_layout.m contains a complete implementation of the person layout task. For each specified person a simple (and not very successful!) layout predictor is trained on the train set; the layout predictor is then applied to theval set and the output saved to a results file in the format required by the challenge; a precision/recall curve is plotted and the `average precision' (AP) measure displayed.

7.3 Non-MATLAB Users

For non-MATLAB users, the file formats used for the VOC2008 data should be straightforward to use in other environments. Image sets (see below) are vanilla text files. Annotation files are XML format and should be readable by any standard XML parser. Images are stored in JPEG format, and segmentation ground truth in PNG format.

8 Using the Development Kit

The development kit provides functions for loading annotation data. Example code for computing precision/recall curves and segmentation accuracy, and for viewing annotation is also provided.

8.1 Image Sets

8.1.1 Classification/Detection Task Image Sets

The VOC2008/ImageSets/Main/ directory contains text files specifying lists of images for the main classification/detection tasks. The files train.txt, val.txt,trainval.txt and test.txt list the image identifiers for the corresponding image sets (training, validation, training+validation and testing). Each line of the file contains a single image identifier. The following MATLAB code reads the image list into a cell array of strings:

imgset='train'; ids=textread(sprintf(VOCopts.imgsetpath,imgset),'%s');

For a given image identifier ids{i}, the corresponding image and annotation file paths can be produced thus:

imgpath=sprintf(VOCopts.imgpath,ids{i}); annopath=sprintf(VOCopts.annopath,ids{i});

Note that the image sets used are the same for all classes. For each competition, participants are expected to provide output for all images in the test set.

8.1.2 Classification Task Image Sets

To simplify matters for participants tackling only the_classification_ task, class-specific image sets with per-image ground truth are also provided. The fileVOC2008/ImageSets/Main/_.txt contains image identifiers and ground truth for a particular class and image set, for example the file car_train.txt applies to the `car' class andtrain image set. Each line of the file contains a single image identifier and ground truth label, separated by a space, for example:

2008_000002 -1
2008_000005  0
2008_000010 -1

The following MATLAB code reads the image list into a cell array of strings and the ground truth label into a corresponding vector:

imgset='train'; cls='car'; [ids,gt]=textread(sprintf(VOCopts.clsimgsetpath, ... cls,imgset),'%s %d');

There are three ground truth labels:

-1:

Negative: The image contains no objects of the class of interest. A classifier should give a `negative' output.

1:

Positive: The image contains at least one object of the class of interest. A classifier should give a `positive' output.

0:

``Difficult'': The image contains only objects of the class of interest marked as `difficult'.

The ``difficult'' label indicates that all objects of the class of interest have been annotated as ``difficult'', for example an object which is clearly visible but difficult to recognize without substantial use of context. Currently the evaluation ignores such images, contributing nothing to the precision/recall curve or AP measure. The final evaluation may include separate results including such ``difficult'' images, depending on the submitted results. Participants are free to omit these images from training or include as either positive or negative examples.

8.1.3 Segmentation Taster Image Sets

The VOC2008/ImageSets/Segmentation/ directory contains text files specifying lists of images for the segmentation taster task. The files train.txt, val.txt,trainval.txt and test.txt list the image identifiers for the corresponding image sets (training, validation, training+validation and testing). Each line of the file contains a single image identifier. The following MATLAB code reads the image list into a cell array of strings:

imgset='train'; ids=textread(sprintf(VOCopts.seg.imgsetpath,imgset),'%s');

For a given image identifier ids{i}, file paths for the corresponding image, annotation, segmentation by object instance and segmentation by class can be produced thus:

imgpath=sprintf(VOCopts.imgpath,ids{i}); annopath=sprintf(VOCopts.annopath,ids{i}); clssegpath=sprintf(VOCopts.seg.clsimgpath,ids{i}); objsegpath=sprintf(VOCopts.seg.instimgpath,ids{i});

Participants are expected to provide output for all images in thetest set.

8.1.4 Person Layout Taster Image Sets

The VOC2008/ImageSets/Layout/ directory contains text files specifying lists of image for the person layout taster task. The files train.txt, val.txt,trainval.txt and test.txt list the image identifiers for the corresponding image sets (training, validation, training+validation and testing). Each line of the file contains a single image identifier, and a single object index. Together these specify a `person' object for which layout is provided or to be estimated, for example:

2008_000026 1 2008_000034 4

The following MATLAB code reads the image list into a cell array of strings and the object indices into a corresponding vector:

imgset='train'; [imgids,objids]=textread(sprintf(VOCopts.layout.imgsetpath, ... VOCopts.trainset),'%s %d');

The annotation for the object (bounding box only in the test data) can then be obtained using the image identifier and object index:

rec=PASreadrecord(sprintf(VOCopts.annopath,imgids{i}));
obj=rec.objects(objids{i});

8.2 Development Kit Functions

8.2.1 VOCinit

The VOCinit script initializes a single structureVOCopts which contains options for the PASCAL functions including directories containing the VOC data and options for the evaluation functions (not to be modified). The field classes lists the object classes for the challenge in a cell array:

VOCopts.classes={'aeroplane','bicycle','bird','boat',... 'bottle','bus','car','cat',... 'chair','cow','diningtable','dog',... 'horse','motorbike','person','pottedplant',... 'sheep','sofa','train','tvmonitor'};

The field trainset specifies the image set used by the example evaluation functions for training:

VOCopts.trainset='train'; % use train for development

Note that participants are free to use both training and validation data in any manner they choose for the final challenge. The field testset specifies the image set used by the example evaluation functions for testing:

VOCopts.testset='val'; % use validation data for development

Other fields provide, for convenience, paths for the image and annotation data and results files. The use of these paths is illustrated in the example implementations.

8.2.1.1 Running on VOC2007 test set.

The flag VOC2007 defined at the start of theVOCinit.m script specifies whether the VOC2007 or VOC2008 data should be used. This changes the directories used for image sets and images and the results directory. To run on the VOC2007 test set, set the flag to ``true'' as indicated in the script. Note that the intention is to train on VOC2008 data and test on VOC2007 data.

8.2.2 PASreadrecord(filename)

The PASreadrecord function reads the annotation data for a particular image from the annotation file specified byfilename, for example:

rec=PASreadrecord(sprintf(VOCopts.annopath,'2008_000026'))

rec =

   folder: 'VOC2008'
 filename: '2008_000026.jpg'
   source: [1x1 struct]
    owner: [1x1 struct]
     size: [1x1 struct]
segmented: 0
  imgname: 'VOC2008/JPEGImages/2008_000026.jpg'
  imgsize: [500 375 3]
 database: 'The VOC2008 Database'
  objects: [1x2 struc

The imgname field specifies the path (relative to the main VOC data path) of the corresponding image. Theimgsize field specifies the image dimensions as(width,height,depth). The database field specifies the data source (VOC2008). The segmented field specifies if a segmentation is available for this image. Thefolder and filename fields provide an alternative specification of the image path, and size an alternative specification of the image size:

rec.size

ans =

 width: 500
height: 375
 depth: 3

The source field contains additional information about the source of the image e.g. web-site and owner. This information is obscured until completion of the challenge. Objects annotated in the image are stored in the struct arrayobjects, for example:

rec.objects(1)

ans =

    class: 'person'
     view: 'Frontal'
truncated: 1
 occluded: 1
difficult: 0
    label: 'PASpersonFrontalTruncOcc'
 orglabel: 'PASpersonFrontalTruncOcc'
     bbox: [122.1875 7.8125 371.5625 375]
   bndbox: [1x1 struct]
  polygon: []
     mask: []
 hasparts: 1
     part: [1x3 struct]

The class field contains the object class. Theview field contains the view:Frontal, Rear, Left (side view, facing left of image), Right (side view, facing right of image), or an empty string indicating another, or un-annotated view. The truncated field being set to 1 indicates that the object is ``truncated'' in the image. The definition of truncated is that the bounding box of the object specified does not correspond to the full extent of the object e.g. an image of a person from the waist up, or a view of a car extending outside the image. Participants are free to use or ignore this field as they see fit. The occluded field being set to 1 indicates that the object is significantly occluded by another object. Participants are free to use or ignore this field as they see fit. The difficult field being set to 1 indicates that the object has been annotated as ``difficult'', for example an object which is clearly visible but difficult to recognize without substantial use of context. Currently the evaluation ignores such objects, contributing nothing to the precision/recall curve. The final evaluation may include separate results including such ``difficult'' objects, depending on the submitted results. Participants may include or exclude these objects from training as they see fit. The bbox field specifies the bounding box of the object in the image, as [left,top,right,bottom]. The top-left pixel in the image has coordinates $(1,1)$. The bndboxfield specifies the bounding box in an alternate form:

rec.objects(1).bndbox

ans =

xmin: 122.1875
ymin: 7.8125
xmax: 371.5625
ymax: 375

For backward compatibility, the label andorglabel fields specify the PASCAL label for the object, comprised of class, view and truncated/difficult flags. The polygon and mask specify polygon/per-object segmentations, and are not provided for the VOC2008 data. The hasparts field specifies if the object has sub-object ``parts'' annotated. For the VOC2008 data, such annotation is available for a subset of the `person' objects, used in the layout taster task. Object parts are stored in the struct arraypart, for example:

rec.objects(1).part(1)

ans =

    class: 'head'
     view: ''
truncated: 0
 occluded: 0
difficult: 0
    label: 'PAShead'
 orglabel: 'PAShead'
     bbox: [193.7966 9.5616 285.6810 130.8302]
   bndbox: [1x1 struct]
  polygon: []
     mask: []
 hasparts: 0
     part: []

The format of object parts is identical to that for top-level objects. For the `person' parts in the VOC2008 data, parts are not annotated with view, or truncated/difficult flags. The bounding box of a part is specified in image coordinates in the same way as for top-level objects. Note that the object parts may legitimately extend outside the bounding box of the parent object.

8.2.3 viewanno(imgset)

The viewanno function displays the annotation for images in the image set specified by imgset. Some examples:

viewanno('Main/train'); viewanno('Main/car_val'); viewanno('Layout/train'); viewanno('Segmentation/val');

8.3 Classification Functions

8.3.1 VOCevalcls(VOCopts,id,cls,draw)

The VOCevalcls function performs evaluation of the classification task, computing a precision/recall curve and the average precision (AP) measure. The argumentsid andcls specify the results file to be loaded, for example:

[rec,prec,ap]=VOCevalcls(VOCopts,'comp1','car',true);

See example_classifier for further examples. If the argument draw is true, the precision/recall curve is drawn in a figure window. The function returns vectors of recall and precision rates in rec and prec, and the average precision measure in ap.

8.4 Detection Functions

8.4.1 VOCevaldet(VOCopts,id,cls,draw)

The VOCevaldet function performs evaluation of the detection task, computing a precision/recall curve and the average precision (AP) measure. The argumentsid andcls specify the results file to be loaded, for example:

[rec,prec,ap]=VOCevaldet(VOCopts,'comp3','car',true);

See example_detector for further examples. If the argument draw is true, the precision/recall curve is drawn in a figure window. The function returns vectors of recall and precision rates in rec and prec, and the average precision measure in ap.

8.4.2 viewdet(id,cls,onlytp)

The viewdet function displays the detections stored in a results file for the detection task. The argumentsid and cls specify the results file to be loaded, for example:

viewdet('comp3','car',true)

If the onlytp argument is true, only the detections considered true positives by the VOC evaluation measure are displayed.

8.5 Segmentation Functions

8.5.1 create_segmentations_from_detections(id,confidence)

This function creates segmentation results from detection results.create_segmentations_from_detections(id) creates segmentations from the detection results with specified identifier e.g. comp3. This is achieved by rendering the bounding box for each detection in class order, so that later classes overwrite earlier classes (e.g. a person bounding box will overwrite an overlapping an aeroplane bounding box). All detections will be used, no matter what their confidence level.create_segmentations_from_detections(id,confidence) does the same, but only detections above the specified confidence will be used. See example_segmenter for an example.

8.5.2 VOCevalseg(VOCopts,id)

The VOCevalseg function performs evaluation of the segmentation task, computing a confusion matrix and segmentation accuracies for the segmentation task. It returns per-class percentage accuracies, the average overall percentage accuracy, and a confusion matrix, for example:

[accuracies,avacc,conf,rawcounts] = VOCevalseg(VOCopts,'comp3')

Accuracies are defined by the intersection/union measure. The optional fourth output `rawcounts' returns an un-normalized confusion matrix containing raw pixel counts. See example_segmenter for another example. This function will also display a table of overall and per-class accuracies.

8.5.3 VOClabelcolormap(N)

The VOClabelcolormap function creates the color map which has been used for all provided indexed images. You should use this color map for writing your own indexed images, for consistency. The size of the color map is given by N, which should generally be set to 256 to include a color for the `void' label.

8.6 Layout Functions

8.6.1 VOCwritexml(rec,path)

The VOCwritexml function writes a MATLAB structure array to a corresponding XML file. It is provided to support the creation of XML results files for the person layout taster. An example of usage can be found in example_layout.

8.6.2 VOCevallayout(VOCopts,id,draw)

The VOCevallayout function performs evaluation of the person layout task, computing a precision/recall curve and the average precision (AP) measure. The argumentsid andcls specify the results file to be loaded, for example:

[rec,prec,ap]=VOCevallayout(VOCopts,'comp6',true);

See example_layout for further examples. If the argumentdraw is true, the precision/recall curve is drawn in a figure window. The function returns vectors of recall and precision rates in rec and prec, and the average precision measure in ap.

Acknowledgements

We gratefully acknowledge the following, who spent many long hours providing annotation for the VOC2008 database: Jan-Hendrik Becker, Patrick Buehler, Kian Ming Chai, Miha Drenik, Chris Engels, Jan Van Gemert, Hedi Harzallah, Nicolas Heess, Zdenek Kalal, Lubor Ladicky, Marcin Marszalek, Alastair Moore, Maria-Elena Nilsback, Paul Sturgess, David Tingdahl, Hirofumi Uemura, Martin Vogt. The preparation and running of this challenge is supported by the EU-funded PASCAL Network of Excellence on Pattern Analysis, Statistical Modelling and Computational Learning.

Bibliography

1

The PASCAL Visual Object Classes Challenge (VOC2008).
http://www.pascal-network.org/challenges/VOC/voc2008/index.html.

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The PASCAL Visual Object Classes Challenge 2008 (VOC2008) Development Kit

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