Image retrieval based on the combination of RGB and HSV's histograms and Colour Layout Descriptor
Main Article Content
Abstract
In this paper we present the first stage of a new approach to improve the precision and recall of the content-based image retrieval task. To do this, we aim to combine three colour features, RGB and HSV histograms, and MPEG- 7 Colour Layout Descriptor. To perform the combination, we propose to use an approximation based on Borda Voting-Schemes. Under that the Borda Voting-Schemes needs at least three votes to perform the combination, we intend to use the K-Nearest Neighbors methods to select the candidate images, given a query image. In the second stage, we’ll implement our approach using at least three image databases.
Keywords
HSV histogram, RGB histogram, Colour layout fescriptor, Borda Voting Schemes, KNN.
References
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T. Kijak, T. Furon, and L. Amsaleg, “Deluding image recognition in SIFT-based CBIR systems,” in Multimedia in Forensics, Security and Intelligence (MiFor), Florence, Italy, October 29 2010.
S. Caperna, C. Cheng, V. Fan, A. Luthra, B. O’Leary, J. Sheng, A. Sun, L. Stearns, R. Tessler, P. Wong, and J. Yeah, “A navigation and object location device for the blind,” University of Maryland, 2009.
N. Singhai and S. Shandilya, “A survey on: Content based image retrieval systems,” International Journal of Computer Applications IJCA, vol. 4, no. 2, pp. 22–26, 2010.
R. Balasubramani and V. Kannan, “Ef- ficient use of MPEG-7 color layout and edge histogram descriptor in CBIR systems,” Global Journal of Computer Science and Technology, vol. 9, no. 4, pp. 157–163, 2009.
S. Berretti, A. Del Bimbo, and P. Pala, “Retrieval by shape similarity with perceptual distance and effective indexing,” IEEE Transactions on Multimedia, vol. 2, no. 4, pp. 225–239, 2000.
T. Deselaers, D. Keysers, and H. Ney, “Features for image retrieval: an experimental comparison,” Information Retrieval, vol. 11, no. 2, pp. 77–107, 2008.
K. Mekaldji, S. Boucherka, and C. S, “Color quantization and its impact on color histogram based image retrieval,” in Procedings of the Second Conference Internationale sur l’Informatique et ses Applications (CIIA’09), Saida, Algeria, May 3 - 4 2009.
J. Miralles, Tutorial de GIMP. [Online]. Available: http: //sites.google.com/site/tutorialdegimp/ 011---teoria-del-color-for% macion-y-mezcla-de-colores -rgb-y-cmyk
S. Jeong, “Histogram-based color image retrieval,” Stanford University, Palo Alto, CA, Psych221/EE362 Project Report, 2001.
A. Vadivel, S. Sural, and A. Majumdar, “Human color perception in the HSV space and its aplication in histogram generation for image retrieval,” in SPIE Procedings seetings, San José CA,United States of America, 2005.
S. Sural, G. Quian, and S. Pramatik, “Segmentation and histogram generation using the HSV color space for image retrieval,” in Procedings International conference on Image Processing, 2002.
University of Auckland. Department of Computer Science, CBIR: Color Features. [Online]. Available: http:// www.cs.auckland.ac.nz/compsci708s1c/ lectures/Glect-html/topic3c708FSC.htm
P. Cunningham and S. Delany, “Knearest neighbors,” Report of the UDC School of Computer Science and Informatics, Dublin, Ireland, Tech. Rep., 2007.
Carleton College, Computer Science Comps Project. Netflix prize. [Online]. Available: http://cs.carleton.edu/cs_ comps/0910/netflixprize/final_results/ knn/index.html
V. Robles, “Esquemas de votación borda aplicados al etiquetado de roles semánticos,” Master’s thesis, Universidad Politécnica de Valencia, Valencia, Spain, 2010.
——, “Borda based voting schemes for semantic role labeling,” in 13th International Conference on Text, Speech, and Dialoge. Lecture Notes in Computer Science, Brno, Czech Republic, September 2010.