Use of Sparse Histogram in Patch Based Generic Visual Categorization

This paper elaborately discusses on effective method for object classification. In the intricate process of object recognition, it so happens that images have often to be classified based on objects which constitute only a very limited part of the image. By using Patches (local features) properly, the properties of certain regions of an image can be described in detail. Then the object parts can be modeled based on the image patches extracted with regard to each salient point, where the information content is high. The information collected thus are, then, properly stored in an histogram. It is proposed to use a sparse representation of the histograms, i.e., only those bins whose content is not empty are stored. The distances between the histograms of the test image and training images are computed by using an appropriate classifier and finally they are classified. The experimental evaluation of the proposed method is carried out using the Caltech database.