Combining generic and class-specific codebooks for object categorization and detection
Combining advantages of shape and appearance features, we propose a novel model that integrates these two complementary features into a common framework for object categorization and detection. In particular, generic shape features are applied as a pre-filter that produces initial detection hypotheses following a weak spatial model, then the learnt class-specific discriminative appearance-based SVM classifier using local kernels verifies these hypotheses with a stronger spatial model and filter out false positives. We also enhance the discriminability of appearance codebooks for the target object class by selecting several most discriminative part codebooks that are built upon a pool of heterogeneous local descriptors, using a classification likelihood criterion. Experimental results show that both improvements significantly reduce the number of false positives and cross-class confusions and perform better than methods using only one cue.