Improving HOG with image segmentation: application to human detection
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Data decomposition and spatial mixture modeling for part based model
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Object class detection: A survey
ACM Computing Surveys (CSUR)
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Object localization is a challenging problem due to variations in object's structure and illumination. Although existing part based models have achieved impressive progress in the past several years, their improvement is still limited by low-level feature representation. Therefore, this paper mainly studies the description of object structure from both feature level and topology level. Following the bottom-up paradigm, we propose a boosted Local Structured HOG-LBP based object detector. Firstly, at feature level, we propose Local Structured Descriptor to capture the object's local structure, and develop the descriptors from shape and texture information, respectively. Secondly, at topology level, we present a boosted feature selection and fusion scheme for part based object detector. All experiments are conducted on the challenging PASCAL VOC2007 datasets. Experimental results show that our method achieves the state-of-the-art performance.