Learning Saliency Maps for Object Categorization

Abstract

Abstract. We present a novel approach for object category recognition that can find objects in challenging conditions using visual attention technique. It combines saliency maps very closely with the extraction of random subwindows for classification purposes. The maps are built online by the classifier while being used by it to classify the image. Saliency is therefore automatically suited to the abilities of the classifier and not an additional concept that is tried to fit into another method. Our results show that we can obtain state of the art results on commonly used datasets with using only little information and thus achieve high efficiency and short processing times. 1