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The Scientific World Journal
Volume 2014 (2014), Article ID 615973, 13 pages
Research Article

Multiscale Distance Coherence Vector Algorithm for Content-Based Image Retrieval

1School of Software, Nanchang Hangkong University, Nanchang 330063, China
2School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China
3School of Information Science and Engineering, Hunan University, Changsha 410082, China

Received 25 August 2013; Accepted 30 January 2014; Published 24 April 2014

Academic Editors: S. Salcedo-Sanz and Y. Wang

Copyright © 2014 Zeng Jiexian 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.


Multiscale distance coherence vector algorithm for content-based image retrieval (CBIR) is proposed due to the same descriptor with different shapes and the shortcomings of antinoise performance of the distance coherence vector algorithm. By this algorithm, the image contour curve is evolved by Gaussian function first, and then the distance coherence vector is, respectively, extracted from the contour of the original image and evolved images. Multiscale distance coherence vector was obtained by reasonable weight distribution of the distance coherence vectors of evolved images contour. This algorithm not only is invariable to translation, rotation, and scaling transformation but also has good performance of antinoise. The experiment results show us that the algorithm has a higher recall rate and precision rate for the retrieval of images polluted by noise.