標題: | Sample Space Dimensionality Refinement for Symmetrical Object Detection |
作者: | Liu, Yun-Fu Guo, Jing-Ming Hsia, Chih-Hsien Su, Sheng-Yao Lee, Hua 電控工程研究所 Institute of Electrical and Control Engineering |
關鍵字: | Sample refinement;dimension reduction;data reduction;face detection;pedestrian detection |
公開日期: | 1-十一月-2014 |
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. |
URI: | http://dx.doi.org/10.1109/TIFS.2014.2355495 http://hdl.handle.net/11536/123945 |
ISSN: | 1556-6013 |
DOI: | 10.1109/TIFS.2014.2355495 |
期刊: | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY |
Issue: | 11 |
起始頁: | 1953 |
結束頁: | 1961 |
顯示於類別: | 期刊論文 |