Abstract
Classic sparse representation, as one of prevalent feature learning methods, is successfully applied for different computer vision tasks. However it has some intrinsic defects in object detection. Firstly, how to learn a discriminative dictionary for object detection is a hard problem. Secondly, it is usually very time-consuming to learn dictionary based features in a traditional exhaustive search manner like sliding window. In this paper, we propose a novel feature learning framework for object detection with the structure sparsity constraint and classification error minimization constraint to learn a discriminative dictionary. For improving the efficiency, we just learn sparse representation coefficients from object candidate regions and feed them to a kernelized SVM classifier. Experiments on INRIA Person Dataset and Pascal VOC 2007 challenge dataset clearly demonstrate the effectiveness of the proposed approach compared with two state-of-the-art baselines.
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Foundation item: Supported by the National Natural Science Foundation of China (61231015, 61170023), National High Technology Research and Development Program of China (863 Program, 2015AA016306), Internet of Things Development Funding Project of Ministry of Industry in 2013 (No. 25), Technology Research Program of Ministry of Public Security (2014JSYJA016), Major Science and Technology Innovation Plan of Hubei Province (2013AAA020), and the Natural Science Foundation of Hubei Province (2014CFB712)
Biography: FANG Wenhua, male, Ph.D. candidate, research direction: multimedia analysis and computer vision.
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Fang, W., Chen, J. & Hu, R. Structural sparse representation for object detection. Wuhan Univ. J. Nat. Sci. 22, 318–322 (2017). https://doi.org/10.1007/s11859-017-1253-2
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DOI: https://doi.org/10.1007/s11859-017-1253-2