Visual Object Categorization Using Combination Rules For Multiple Classifiers

This paper discusses the ensemble learning algorithms of Support Vector Machines (SVMs) by combining multiple edge and orientation descriptors based on several basic primitive filters. The methodology divided into two parts. the first section provides a low-level description for describing images. In this part, the proposed method starts with constructing several feature maps of the input image. The maps are constructed by convolving the image data using a set of M different filters namely Robinson filters resulting M different feature maps. After that, the edge and orientation descriptors namely edge histogram, histograms of oriented gradients and Scale Invariant Feature Transform (SIFT) on fixed grid are used to describe the maps for indexing. The second part provides the image classification task. In this part, the support vector machine algorithm is used to train models of different filter based descriptors. After that, all models are used to estimate the correct output class by using the class probabilities of all trained support vector models. In this case, single or individual classifier, naive classifier and ensemble learning classifiers namely product rule, mean rule and majority voting rule are used to measure the recognition performance. Experimental results on 20 classes of the Caltech-101 object dataset prove that the ensemble approaches superior single and naive approaches at about 71.23% and 60.67% of accuracy rates subsequently.