Semantic Concepts Classification on Outdoor Scene Images Based on Region-Based Approach (original) (raw)
Related papers
IJERT-Semantic Categorization and Retrieval of Natural Scene Images
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/semantic-categorization-and-retrieval-of-natural-scene-images https://www.ijert.org/research/semantic-categorization-and-retrieval-of-natural-scene-images-IJERTV2IS100944.pdf In this paper, we present an approach for the retrieval of natural scenes based on a semantic modelling. In this Semantic modelling stands for the classification of local image regions into semantic classes as grass, rocks or foliage and the subsequent summary in the local image regions are classified using low level features into semantic concept classes such as water, sky or sand. This paper proposes a method for semantic categorization and retrieval of natural scene images or picture with and without people. Image retrieval systems usually represent images by a collection of low-level features such as colour, texture, edge positions and spatial relationships in the image. These features are used to compute the similarity between a picture or image selected by the user and the picture or images in the database. We have attempts to provide a comprehensive survey of the latest mechanical achievements in high-level semantic-based image retrieval.
Semantic multimedia analysis based on region types and visual context
… and Innovations 2007: from Theory to …, 2007
In this paper previous work on the detection of high-level concepts within multimedia documents is extended by introducing a mid-level ontology as a means of exploiting the visual context of images in terms of the regions they consist of. More specifically, we construct a mid-level ontology, define its relations and integrate it in our knowledge modelling approach. In the past we have developed algorithms to address computationally efficient handling of visual context and extraction of mid-level characteristics and now we explain how these diverse algorithms and methodologies can be combined in order to approach a greater goal, that of semantic multimedia analysis. Early experimental results are presented using data derived from the beach domain.
Pattern Recognition and Image Analysis, 2016
A large percentage of photos on the Internet cannot be reached by search engines because of the semantic gap due to the absence of textual meta data. Despite of decades of research, neither model based approaches can provide quality annotation to images. Many segmentation algorithms use a low level predi cates to control the homogeneity of the regions. So, the resulting regions are not always being semantically compact. The first proposed approach to resolve this problem is to regroup the adjacent region of image. Many features extraction method and classifiers are also used singly, with modest results, for automatic image annotation. The second proposed approach is to select and combine together some efficient descriptors and classifiers. This document provides a hybrid semantic annotation system that combines both approaches in hopes of increasing the accuracy of the resulting annotations. The color histograms, Texture, GIST and invariant moments, used as features extraction methods, are combined together with multi class support vec tor machine, Bayesian networks, Neural networks and nearest neighbor classifiers, in order to annotate the image content with the appropriate keywords. The accuracy of the proposed approach is supported by the good experimental results obtained from two image databases (ETH 80 and coil 100 databases).
Semantic Scene Classification for Image Annotation
Nowadays The problem of annotating an image falls under pattern classification and computer vision methods. Various systems have been proposed for the purpose of image annotation, content based retrieval, and image classification and indexing. Most of these systems are based on low level features such as color and texture statistics. We believe that the detection of semantically meaningfully regions in digital photographs could be productively employed in classification and retrieval systems for image databases. The image retrieval problem has not been successfully solved in spite of decades of work, dozens of research prototypes and commercial products that have been developed lately. In addition to its primary goals, consistent, cost-effective, fast, intelligent annotation of visual data, the method proposed can also be used to improve the performance of subsequent image query and retrieval operations on the annotated image repository. Pure content based image retrieval (CBIR) also falls short of providing an adequate solution to the problem, mostly due to the limitations of current computer vision techniques in bridging the gap between visual features and their semantic meaning. There is a clear need to cleverly combine the two in order to yield a better solution.
Using region semantics and visual context for scene classification
Image Processing, 2008. …, 2008
In this paper we focus on scene classification and detection of high-level concepts within multimedia documents, by introducing an intermediate contextual approach as a means of exploiting the visual context of images. More specifically, we introduce and model a novel relational knowledge representation, founded on topological and semantic relations between the concepts of an image. We further develop an algorithm to address computationally efficient handling of visual context and extraction of mid-level region characteristics. Based on the proposed knowledge model, we combine the notion of visual context with region semantics, in order to exploit their efficacy in dealing with scene classification problems. Finally, initial experimental results are presented, in order to demonstrate possible applications of the proposed methodology.
Recognition of Semantic Content in Image and Video
International Journal of Computer Applications, 2013
This paper addresses the problem of recognizing semantic content from images and video for content based retrieval purposes. Semantic features are derived from a collection of low-level features based on color, texture and shape combined together to form composite feature vectors. Both Manhattan distance and Neural Networks are used as classifiers for recognition purposes. Discrimination is done using five semantic classes viz. mountains, forests, flowers, highways and buildings. The composite feature is represented by a 26-element vector comprising of 18 color components, 2 texture components and 6 shape components.
Mapping low-level image features to semantic concepts
SPIE Proceedings, 2001
Humans tend to use high-level semantic concepts when querying and browsing multimedia databases; there is thus, a need for systems that extract these concepts and make available annotations for the multimedia data. The system presented in this paper satisfies this need by automatically generating semantic concepts for images from their low-level visual features. The proposed system is built in two stages. First, an adaptation of k-means clustering using a non-Euclidean similarity metric is applied to discover the natural patterns of the data in the low-level feature space; the cluster prototype is designed to summarize the cluster in a manner that is suited for quick human comprehension of its components. Second, statistics measuring the variation within each cluster are used to derive a set of mappings between the most significant low-level features and the most frequent keywords of the corresponding cluster. The set of the derived rules could be used further to capture the semantic content and index new untagged images added to the image database. The attachment of semantic concepts to images will also give the system the advantage of handling queries expressed in terms of keywords and thus, it reduces the semantic gap between the user's conceptualization of a query and the query that is actually specified to the system. While the suggested scheme works with any kind of low-level features, our implementation and description of the system is centered on the use of image color information. Experiments using a 21 00 image database are presented to show the efficacy of the proposed system.
Semantic Approach to Image Database Classification and Retrieval
2003
This paper demonstrates an approach to image retrieval founded on classifying image regions hierarchically based on their semantics (e.g., sky, snow, rocks, etc.) that resemble peoples' perception rather than on low-level features (e.g., color, texture, shape, etc.). Particularly, we consider to automatically classify regions of outdoor images based on their semantics using the support vector machines (SVMs) tool. First, image regions are segmented using the hill-climbing method. Then, those regions are classified by the SVMs. The SVMs learn the semantics of specified classes from a test database of image regions. Such semantic classification allows the implementation of intuitive query interface. As we show in our experiments, the high precision of semantic classification justifies the feasibility of our approach.
Low-level Image Segmentation Based Scene Classification
2010 International Conference on Pattern …, 2010
This paper is aimed at evaluating the semantic information content of multiscale, low-level image segmentation. As a method of doing this, we use selected features of segmentation for semantic classification of real images. To estimate the relative measure of the information content of our features, we compare the results of classifications we obtain using them with those obtained by others using the commonly used patch/grid based features. To classify an image using segmentation based features, we model the image in terms of a probability density function, a Gaussian mixture model (GMM) to be specific, of its region features. This GMM is fit to the image by adapting a universal GMM which is estimated so it fits all images. Adaptation is done using a maximum-aposteriori criterion. We use kernelized versions of Bhattacharyya distance to measure the similarity between two GMMs and support vector machines to perform classification. We outperform previously reported results on a publicly available scene classification dataset. These results suggest further experimentation in evaluating the promise of low level segmentation in image classification.
Multiple Region Categorization for Scenery Images
Lecture Notes in Computer Science, 2011
We present two novel contributions to the problem of region classification in scenery/landscape images. The first is a model that incorporates local cues with global layout cues, following the statistical characteristics recently suggested in . The observation that background regions in scenery images tend to horizontally span the image allows us to represent the contextual dependencies between background region labels with a simple graphical model, on which exact inference is possible. While background is traditionally classified using only local color and textural features, we show that using new layout cues significantly improves background region classification. Our second contribution addresses the problem of correct results being considered as errors in cases where the ground truth provides the structural class of a land region (e.g., mountain), while the classifier provides its coverage class (e.g., grass), or vice versa. We suggest an alternative labeling method that, while trained using ground truth that describes each region with one label, assigns both a structural and a coverage label for each land region in the validation set. By suggesting multiple labels, each describing a different aspect of the region, the method provides more information than that available in the ground truth.