tailieunhanh - Báo cáo hóa học: " Research Article Face Retrieval Based on Robust Local Features and Statistical-Structural Learning Approa"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Face Retrieval Based on Robust Local Features and Statistical-Structural Learning Approa | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 631297 12 pages doi 2008 631297 Research Article Face Retrieval Based on Robust Local Features and Statistical-Structural Learning Approach Daidi Zhong and Irek Defee Institute of Signal Processing Tampere University of Technology . Box 553 33101 Tampere Finland Correspondence should be addressed to Irek Defee Received 30 September 2007 Revised 15 January 2008 Accepted 17 March 2008 Recommended by Sébastien Lefevre A framework for the unification of statistical and structural information for pattern retrieval based on local feature sets is presented. We use local features constructed from coefficients of quantized block transforms borrowed from video compression which robustly preserving perceptual information under quantization. We then describe statistical information of patterns by histograms of the local features treated as vectors and similarity measure. We show how a pattern retrieval system based on the feature histograms can be optimized in a training process for the best performance. Next we incorporate structural information description for patterns by considering decomposition of patterns into subareas and considering their feature histograms and their combinations by vectors and similarity measure for retrieval. This description of patterns allows flexible varying of the amount of statistical and structural information it can also be used with training process to optimize the retrieval performance. The novelty of the presented method is in the integration of information contributed by local features by statistics of feature distribution and by controlled inclusion of structural information which are combined into a retrieval system whose parameters at all levels can be adjusted by training which selects contribution of each type of information best for the overall retrieval performance. The proposed framework is .