Content-based object movie retrieval and relevance feedbacks

Authors:
Cheng-Chieh Chiang;Li-Wei Chan;Yi-Ping Hung;Greg C. Lee
Affiliations:
Graduate Institute of Information and Computer Education, College of Education, National Taiwan Normal University, Taipei, Taiwan and Department of Information Technology, Takming College, Taipei, ...;Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan;Graduate Institute of Networking and Multimedia, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan;Department of Computer Science and Information Engineering, College of Science, National Taiwan Normal University, Taipei, Taiwan
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2007

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Abstract

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 presented to show the efficacy of our approach.