Content-Based Object Movie Retrieval and Relevance Feedbacks. (2nd July 2007)

• Record Type: Journal Article
• Authors: Chiang Chiang, Cheng-Chieh Cheng-Chieh; Chan Chan, Li-Wei Li-Wei; Hung Hung, Yi-Ping Yi-Ping; Lee Lee, Greg C. Greg C.
• Other Names: Chen Chen Tsuhan Tsuhan Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2007-07-02
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/89691
• Languages: English
• ISSNs: 1687-6172

Abstract

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.