Sample Space Dimensionality Refinement for Symmetrical Object Detection

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題名:	Sample Space Dimensionality Refinement for Symmetrical Object Detection
作者:	Liu, Yun-Fu Guo, Jing-Ming Hsia, Chih-Hsien Su, Sheng-Yao Lee, Hua
貢獻者:	電機系
關鍵詞:	Sample refinement dimension reduction data reduction face detection pedestrian detection
日期:	2014-11
上傳時間:	2015-01-19 15:14:34 (UTC+8)
摘要:	Formerly, dimensionality reduction techniques are effective ways for extracting statistical significance of features from their original dimensions. However, the dimensionality reduction also induces an additional complexity burden which may encumber the real efficiency. In this paper, a technique is proposed for the reduction of the dimension of samples rather than the features in the former schemes, and it is able to additionally reduce the computational complexity of the applied systems during the reduction process. This method effectively reduces the redundancies of a sample, in particular for those objects which possess partially symmetric property, such as human face, pedestrian, and license plate. As demonstrated in the experiments, based upon the premises of faster speeds in training and detection by a factor of 4.06 and 1.24, respectively, similar accuracies to the ones without considering the proposed method are achieved. The performance verifies that the proposed technique can offer competitive practical values in pattern recognition related fields.
關聯:	IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY Volume: 9 Issue: 11 Pages: 1953-1961
顯示於類別:	[電機工程系] 期刊論文

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