tailieunhanh - Báo cáo hóa học: " Research Article Content-Based Object Movie Retrieval and Relevance Feedbacks"

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 Content-Based Object Movie Retrieval and Relevance Feedbacks | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 89691 9 pages doi 2007 89691 Research Article Content-Based Object Movie Retrieval and Relevance Feedbacks Cheng-Chieh Chiang 1 2 Li-Wei Chan 3 Yi-Ping Hung 4 and Greg C. Lee5 1 Graduate Institute of Information and Computer Education College of Education National Taiwan Normal University Taipei 106 Taiwan 2 Department of Information Technology Takming College Taipei 114 Taiwan 3 Department of Computer Science and Information Engineering College of Electrical Engineering and Computer Science National Taiwan University Taipei 106 Taiwan 4 Graduate Institute of Networking and Multimedia College of Electrical Engineering and Computer Science National Taiwan University Taipei 106 Taiwan 5 Department of Computer Science and Information Engineering College of Science National Taiwan Normal University Taipei 106 Taiwan Received 26 January 2006 Revised 19 November 2006 Accepted 13 May 2007 Recommended by Tsuhan Chen Object movie refers to a set of images captured from different perspectives around a 3D object. Object movie provides a good representation of a physical object because it can provide 3D interactive viewing effect but does not require 3D model reconstruction. In this paper we propose an efficient approach for content-based object movie retrieval. In order to retrieve the desired object movie from the database we first map an object movie into the sampling of a manifold in the feature space. Two different layers of feature descriptors dense and condensed are designed to sample the manifold for representing object movies. Based on these descriptors we define the dissimilarity measure between the query and the target in the object movie database. The query we considered can be either an entire object movie or simply a subset of views. We further design a relevance feedback approach to improving retrieved results. Finally some experimental results are .