Improving a Discriminative Approach to Object Recognition Using Image Patches

In this paper we extend a method that uses image patch histograms and discriminative training to recognize objects in cluttered scenes. The method generalizes and performs well for different tasks, e.g. for radiograph recognition and recognition of objects in cluttered scenes. Here, we further investigate this approach and propose several extensions. Most importantly, the method is substantially improved by adding multi-scale features so that it better accounts for objects of different sizes. Other extensions tested include the use of Sobel features, the generalization of histograms, a method to account for varying image brightness in the PCA domain, and SVMs for classification. The results are improved significantly, i.e. on average we have a 59% relative reduction of the error rate and we are able to obtain a new best error rate of 1.1% on the Caltech motorbikes task.