Scene Classification Using Adaptive Integration of Reconstruction Errors
This paper proposes an adaptive integration method of reconstruction errors obtained from different point of view. There are some methods for integrating local and global processing. However, integration parameters are fixed for all test samples though effective parameters are different for every sample. Therefore, we select adaptively the parameters from only a test image. In static image recognition, the information of an input image is not changed. However, the posterior probability in weight space is changed for every test image. Thus, the posterior probability in weight space is estimated by a particle filter, and effective weight with high probability for the test sample is selected. Experimental results demonstrate the effectiveness of our approach.
Kazuhiro HOTTA
Meijo University 1-501 Shiogamaguchi, Tenpaku-ku, Nagoya 468-8502, JAPAN
国际会议
北京
英文
154-158
2011-11-28（万方平台首次上网日期，不代表论文的发表时间）