Abstract—We develop a method for combined object detection and segmentation in natural scene. In our approach segmentation and detection are considered as two faces of the same coin that should be combined into a single framework. There are two main steps in our strategy. First we focus on the learning of a visual vocabulary that efficiently encompasses objects’ appearance, spatial configuration and underlying segmentation. This vocabulary is used within a Hough voting framework to produces object’s configuration. The second step consists in searching for valid objects’ configurations by interpreting and scoring them in terms of both detection and segmentation. This allows us to prune false detections and hallucinated object-like segmentation. Experiments show the advantage of the combined approach and the improvements over recent related methods.
Index Terms—Object recognition, random forest, hough votes.
J. Vansteenberge is with the Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto, Japan. (e-mail: vansteenberge@mm.kyoto-u.ac.jp). M. Mukunoki and M. Minoh are with the Academic Center for Computing and Media Studies, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan.
Cite:Jarich Vansteenberge, Masayuki Mukunoki, and Michihiko Minoh, "Combined Object Detection and Segmentation," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 60-64, 2013.