Boosted Local Structured HOG-LBP for Object Localization

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 HOGLBP 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.

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Junge Zhang, Kaiqi Huang, Tieniu Tan
• Keywords: Computer Vision, CVPR 2011, Feature Level, Low-level Feature Representation, Topology Level