Circular Blurred Shape Model for Multiclass Symbol Recognition (original) (raw)

Multi-class binary symbol classification with circular blurred shape models

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009

Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-ofthe-art descriptors, showing significant performance improvements.

Blurred Shape Model for binary and grey-level symbol recognition

Pattern Recognition Letters, 2009

Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance.

Aligning Bags of Shape Contexts for Blurred Shape Model Based Symbol Classification

Abstract This paper addresses the problem of shape classification and proposes a method able to exploit peculiarities of both, local and global shape descriptors. In the proposed shape classification framework, the silhouettes of symbols are firstly described through Bags of Shape Contexts. This shape signature is used to solve correspondence problem between points of two shapes.

Multi-class Binary Object Categorization Using Blurred Shape Models

Lecture Notes in Computer Science, 2008

The main difficulty in the binary object classification field lays in dealing with a high variability of symbol appearance. Rotation, partial occlusions, elastic deformations, or intra-class and inter-class variabilities are just a few problems. In this paper, we introduce a novel object description for this type of symbols. The shape of the object is aligned based on principal components to make the recognition invariant to rotation and reflection. We propose the Blurred Shape Model (BSM) to describe the binary objects. This descriptor encodes the probability of appearance of the pixels that outline the object's shape. Besides, we present the use of this descriptor in a system to improve the BSM performance and deal with binary objects multi-classification problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split object classes. Then, the different binary problems learned by the Adaboost are embedded in the Error Correcting Output Codes framework (ECOC) to deal with the muti-class case. The methodology is evaluated in a wide set of object classes from the MPEG07 repository. Different state-of-the-art descriptors are compared, showing the robustness and better performance of the proposed scheme when classifying objects with high variability of appearance.

Hand drawn symbol recognition by blurred shape model descriptor and a multiclass classifier

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008

In the document analysis field, the recognition of handwriting symbols is a difficult task because of the distortions due to hand drawings and the different writer styles. In this paper, we propose the Blurred Shape Model to describe handwritten symbols, and the use of Adaboost in an Error Correcting Codes framework to deal with multi-class categorization handwriting problems. It is a robust approach tolerant to the distortions and variability typically found in handwritten documents. This approach has been evaluated with the public GREC2005 database and an architectural symbol database extracted from a sketching interface, reaching high recognition rates compared with the state-of-the-art approaches.

Aligning shapes for symbol classification and retrieval

This paper proposes a method able to exploit peculiarities of both, local and global shape descriptors, to be employed for shape classification and retrieval. In the proposed framework, the silhouettes of symbols are firstly described through Bags of Shape Contexts. The shape signature is then used to solve the correspondence problem between points of two shapes. The obtained correspondences are employed to recover the geometric transformations between the shape to be classified/retrieved and the ones belonging to the training dataset. The alignment is based on a voting procedure in the parameter space of the model considered to recover the geometric transformation. The aligned shapes are finally described with the Blurred Shape Model descriptor for classification and retrieval purposes. Experimental results demonstrate the effectiveness of the proposed solution on two classic benchmark shape datasets, as well as on a large scale set of hand sketches composed by 20,000 examples distributed over 250 object categories.

The recognition of shapes in binary images using a gradient classifier

IEEE Transactions on Systems, Man, and Cybernetics, 1989

A prototype-based shape classifier that makes comparisons based on blurred representations of binary images is considered. The blurring induces a metric on the space of all images that varies continuously under continuous deformation of the image piane. This blurred representation is suitable for direct implementation of a nearest neighbor classifier. However, it is still desirable to have a representation that is invariant under certain spatial deformations, such as rotation, translation, and scaling of the image plane. A representation that is invariant under these transformations is produced by transforming an input to a local minimum of its distance from each prototype simultaneously. These minima are found by performing a gradient descent on an appropriate e m r surface defined over the transformation parameters. The error functional is the Ip,-norm of the difference between the blurred prototype and the blurred input. The resulting classifier makes more efficient use of prototypes than the nearest neighbor classifier.

Handwritten Symbol Recognition by a Boosted Blurred Shape Model with Error Correction

2007

One of the major difficulties of handwriting recognition is the variability among symbols because of the different writer styles. In this paper we introduce the boosting of blurred shape models with error correction, which is a robust approach for describing and recognizing handwritten symbols tolerant to this variability. A symbol is described by a probability density function of blurred shape model that encodes the probability of pixel densities of image regions. Then, to learn the most distinctive features among symbol classes, boosting techniques are used to maximize the separability among the blurred shape models. Finally, the set of binary boosting classifiers is embedded in the framework of Error Correcting Output Codes (ECOC). Our approach has been evaluated in two benchmarking scenarios consisting of handwritten symbols. Compared with state-of-the-art descriptors, our method shows higher tolerance to the irregular deformations induced by handwritten strokes.

Robust Symbol Recognition using a Structural Approach Mathieu Delalandre CVC (Barcelona, Spain)

In this paper we present a robust system of symbol recognition using a structural approach. Our key objective here is to provide a system, equaling the statistical ones in robustness concerning the recognition, to apply next to localization. To do it we have investigated two particular structural methods: the straight line detection using Hough Transform and the vector templates matching. Experiments done on the GREC2003 database show how their combination allows to obtain high recognition results.

Logo Recognition System Using Angular Radial Transform Descriptors

2011

Problem statement: The shape-based logo recognition systems have been developed to automate the logo registration process. The logo recognition operation faces many challenges such as having to recognize logos that might be scaled, rotated, translated and added with noises. Different types of logo's shapes further add to the complex nature of this problem. Approach: We developed a logo recognition system that comprises of three phases: Preprocessing, feature extraction and features matching. For feature extraction, we adopted a region-based Angular Radial Transform (ART) to extract the features from logo's shapes. We used the Euclidian Distance (ED) as a similarity measure parameter for the features matching. Results: We tested the system that used the ART as feature extraction method on a large logo database of 2730 images to investigate the effect of several deformations and noise on recognition performance. The experimental results showed the system that use the ART features was robust against the size changing, had an excellent discrimination power against different types of noises and good immunity to rotations. The performance evaluation results showed that ART technique perform better than Zernike moments and Invariant moment's techniques. Conclusion: The proposed ART descriptor was very effective to describe all types of logo's shapes independent on different types of deformations and noise. It also represented the logo's shapes in concise manner without information redundancy.