tailieunhanh - báo cáo hóa học:" Research Article Exploiting Textons Distributions on Spatial Hierarchy for Scene Classification"

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 Exploiting Textons Distributions on Spatial Hierarchy for Scene Classification | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2010 Article ID 919367 13 pages doi 2010 919367 Research Article Exploiting Textons Distributions on Spatial Hierarchy for Scene Classification S. Battiato G. M. Farinella G. Gallo and D. Ravi Image Processing Laboratory University of Catania 95125 Catania Italy Correspondence should be addressed to G. M. Farinella gfarineUa@ Received 29 April 2009 Revised 24 November 2009 Accepted 10 March 2010 Academic Editor Benoit Huet Copyright 2010 S. Battiato et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. This paper proposes a method to recognize scene categories using bags of visual words obtained by hierarchically partitioning into subregion the input images. Specifically for each subregion the Textons distribution and the extension of the corresponding subregion are taken into account. The bags of visual words computed on the subregions are weighted and used to represent the whole scene. The classification of scenes is carried out by discriminative methods . SVM KNN . A similarity measure based on Bhattacharyya coefficient is proposed to establish similarities between images represented as hierarchy of bags of visual words. Experimental tests using fifteen different scene categories show that the proposed approach achieves good performances with respect to the state-of-the-art methods. 1. Introduction The automatic recognition of the context of a scene is a useful task for many relevant computer vision applications such as object detection and recognition 1 content-based image retrieval CBIR 2 or bootstrap learning to select the advertising to be sent by Multimedia Messaging Service MMS 3 4 . Existing methods work on extracting local concepts directly on spatial domain 2 5-7 or frequency domain 8 9