Generalized-hough-transform object detection using class-specific sparse representation for local-feature detection

We present a method for object detection based on sparse representations and Hough voting, which integrates sparse representations for local-feature detection into generalized-Hough-transform object detection. Object parts are detected via class-specific sparse image representations of patches using learned target and background dictionaries, and their cooccurrence is spatially integrated by Hough voting, which enables object detection. In this paper, a discriminative criterion is introduced into dictionary construction to improve the detection performance. Experiments performed on airplane detection and the identification of a specific ship show that the proposed method achieves state-of-the-art performance with the robustness against noise and occlusion using a small set of positive training samples.