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Journal of Applied Mathematics
Volume 2013, Article ID 785824, 6 pages
Research Article

A Novel Optimization-Based Approach for Content-Based Image Retrieval

1Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China
2Computer Science and Technology, Beihang University, Beijing 100191, China
3Engineering Simulation and Aerospace Computing (ESAC), Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China

Received 4 October 2013; Accepted 5 December 2013

Academic Editor: Feng Gao

Copyright © 2013 Manyu Xiao 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.


Content-based image retrieval is nowadays one of the possible and promising solutions to manage image databases effectively. However, with the large number of images, there still exists a great discrepancy between the users’ expectations (accuracy and efficiency) and the real performance in image retrieval. In this work, new optimization strategies are proposed on vocabulary tree building, retrieval, and matching methods. More precisely, a new clustering strategy combining classification and conventional -Means method is firstly redefined. Then a new matching technique is built to eliminate the error caused by large-scaled scale-invariant feature transform (SIFT). Additionally, a new unit mechanism is proposed to reduce the cost of indexing time. Finally, the numerical results show that excellent performances are obtained in both accuracy and efficiency based on the proposed improvements for image retrieval.