L1-norm latent SVM for compact features in object detection

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• 作者：Min Tan^a ; ^{tanmin@zju.edu.cn" class="auth_mail}Author Vitae ; Gang Pan^a ; ^{gpan@zju.edu.cn" class="auth_mail}Author Vitae ; Yueming Wang^b ; ^{Ymingwang@gmail.com" class="auth_mail}Author Vitae ; Yuting Zhang^a ; ^{zyt@zju.edu.cn" class="auth_mail}Author Vitae ; Zhaohui Wu^a ; ^{wzh@zju.edu.cn" class="auth_mail}Author Vitae
• 关键词：Object detection ; Feature reduction ; Sparse ; L1 optimization ; Stochastic gradient descent ; Cascade
• 刊名：Neurocomputing
• 出版年：2 September 2014
• 年：2014
• 卷：139
• 期：Complete
• 页码：56-64
• 全文大小：1783 K

文摘

The deformable part model is one of the most effective methods for object detection. However, it simultaneously computes the scores for a holistic filter and several part filters in a relatively high-dimensional feature space, which causes the problem of low computational efficiency. This paper proposes an approach to select compact and effective features by learning a sparse deformable part model using L1-norm latent SVM. A stochastic truncated sub-gradient descent method is presented to solve the L1-norm latent SVM problem. Convergence of the algorithm is proved. Extensive experiments are conducted on the INRIA and PASCAL VOC 2007 datasets. A highly compact feature in our method can reach the state-of-the-art performance. The feature dimensionality is reduced to 12% of the original one in the INRIA dataset and less than 30% in most categories of PASCAL VOC 2007 dataset. Compared with the features used in L2-norm latent SVM, the average precisions (AP) have almost no drop using the reduced feature. With our method, the speed of the detection score computation is faster than that of the L2-norm latent SVM method by 3 times. When the cascade strategy is applied, it can be further speeded up by about an order of magnitude.