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CVPR
2009
IEEE

Recognition using Regions

14 years 11 months ago
Recognition using Regions
This paper presents a unified framework for object detection, segmentation, and classification using regions. Region features are appealing in this context because: (1) they encode shape and scale information of objects naturally; (2) they are only mildly affected by background clutter. Regions have not been popular as features due to their sensitivity to segmentation errors. In this paper, we start by producing a robust bag of overlaid regions for each image using Arbel ´aez et al., CVPR 2009. Each region is represented by a rich set of image cues (shape, color and texture). We then learn region weights using a max-margin framework. In detection and segmentation, we apply a generalized Hough voting scheme to generate hypotheses of object locations, scales and support, followed by a verification classifier and a constrained segmenter on each hypothesis. The proposed approach significantly outperforms the state of the art on the ETHZ shape database (87.1% average detec...
Chunhui Gu, Joseph J. Lim, Pablo Arbelaez, Jitendr
Added 05 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Chunhui Gu, Joseph J. Lim, Pablo Arbelaez, Jitendra Malik
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