Multiclass object detection by combining local appearances and context

摘要

In this paper, we present a novel approach for multiclass object detection by combining local appearances and contextual constraints. We first construct a multiclass Hough forest of local patches, which can well deal with multiclass object deformations and local appearance variations, due to randomization and discrimination of the forest. Then, in the object hypothesis space, a new multiclass context model is proposed to capture relative location constraints, disambiguating appearance inputs in multiclass object detection. Finally, multiclass objects are detected with a greedy search algorithm efficiently. Experimental evaluations on two image data sets show that the combination of local appearances and context achieves state-of-the-art performance in multiclass object detection.

巫义锐

青年教授, CCF 高级会员

My research interests include Computer Vision, Artifical Intelligence, Multimedia Computing and Intelligent Water Conservancy.