NDLI: Boosted local structured HOG-LBP for object localization

Content Provider	IEEE Xplore Digital Library
Author	Junge Zhang Kaiqi Huang Yinan Yu Tieniu Tan
Copyright Year	2011
Description	Author affiliation: National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences (Junge Zhang; Kaiqi Huang; Yinan Yu; Tieniu Tan)
Abstract	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.
Starting Page	1393
Ending Page	1400
File Size	373846
Page Count	8
File Format	PDF
ISBN	9781457703942
ISSN	10636919
e-ISBN	9781457703959
DOI	10.1109/CVPR.2011.5995678
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Histograms Detectors Training Computational modeling Robustness Support vector machines
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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