The Effect of Region Segmentation on Object Categorization

The success of object categorization is heavily dependent on the extracted image descriptors. In general, image or region segmentation is usually performed to segment an image into several regions or objects, and then some level-level features, such as color and texture, are extracted from each region. As a result, the region descriptor or the combination of multiple region descriptors can be used to represent a specific object or the whole image for categorization. Since there are many well-known region segmentation algorithms proposed in literature, and using different region segmentation algorithms can produce different region descriptors for the same images, no study examines the effect of region segmentation on object categorization. In this paper, we apply three well-known region segmentation algorithms for image feature extraction and representation, which are graph cuts, mean-shift segmentation, and normalized cuts. Then, the support vector machine (SVM) is used as the classifier for object categorization. Our experimental results based on Caltech 5, Caltech 8, and Corel 10 datasets show that the normalized cuts algorithm performs best. In addition, the image feature representation based on multiple region descriptors can provide more discriminative power than using center region descriptors.