Object Retrieval Using Image Semantic Structure Groupings

This paper explores basic level of semantic structure formation in the human vision inferential processes in line with Gestalt laws and proposes micro level semantic structure formations and their relational combinations. Using this approach two sets of semantic features have been derived for visual object class recognition. The first algorithm uses the hypothesis in line with Gestalt laws of proximity that; in an image, basic semantic structures are formed by line segments ( arcs also approximated and broken into smaller line segments based on pixel deviation threshold) which are in close proximity of each other. Based on the notion of proximity a transitive relation is defined, which combines basic micro level semantic structures hierarchically till such a point where semantic meanings of the structure can be extracted. The algorithm extracts line segments in an image and then forms semantic groups of these line segments based on a minimum distance threshold from each other. The line segment groups so formed can be differentiated from each other, by the number of group members and their geometrical properties. The geometrical properties of these semantic groups are used to generate rotation, translation and scale invariant histograms used as feature vectors for object class recognition tasks in a K-nearest neighbor framework. In the second approach a semantic group based on the proximity distance is clustered and modeled as a graph vertex. The line segments which are common to more than one semantic group are defined as semantic relations between the semantic groups and are modeled as edges of the graph. This way an image object is transformed into a graph using micro level structure formations. Each vertex and edge is labeled using translation, rotation and scale invariant properties of the member segments of each vertex and edge. From a set of training images, a graph model is constructed for visual object class recognition. The graph model is constructed by iteratively combining the training graphs and frequency labeling the vertices and edges. After the combining phase, all the vertices and edges whose repetition frequency is below a threshold are removed. The final graph model consists of the semantic nodes which are highly common in the training images. The recognition is based on graph matching the query image graph and the model graph. The model graph generates a vote for the query and ties are resolved by considering the node frequencies in the query and model graph. The algorithms have been applied to classify 101 object classes at one time. The results have been compared with existing state of the art approaches and are found promising. Results from above approaches show that low level image structure and other features can be used to construct different type of semantic features, which can help a model or a classifier make more intelligent decisions and work more effectively for the task compared to low level features alone. Our experimental results are comparable, or outperform other state-of-the-art approaches. We have also summarized the state-of-the-art at the time this work was finished. We conclude with a discussion about the possible future extensions.