Content-Based Image Retrieval Hybrid Approach using Artificial Bee Colony and K-means Algorithms
Keywords:
Contents-based image retrieval, CBIR, Feature Extraction, Clustering, Artificial bee colony, K-means clustering algorithm.Abstract
In this paper, a new clustering method is proposed for CBIR system; this method depends on combining ABC and k-means algorithm. Four features are used with the proposed method to retrieve the images. These features are extracted by: color histogram of HSV image and color histogram of opponent image to describe the color, Gabor filters and Ranklet transform for RGB image to describe the texture. The proposed hybrid clustering method is a clustering process for database of each feature using k-means algorithm enhanced by ABC algorithm. The innovation in this approach is that each solution in ABC algorithm represents the centroids of clusters that come out from applying k-means algorithm. The proposed method is applied on Wang dataset (1000 images in 10 classes) and evaluated by comparing the test results of the proposed scheme with another existing method uses same database. The results proved that the proposed method is superior to the existing method in terms of the precision in 6 out of 10 categories of WANG dataset, such that the average of the precisions for all categories is 0.8093.
References
A. Ahmadyfard and H. Modares, "Combining PSO and k-means to Enhance Data Clustering," in Telecommunications, 2008. IST 2008. International Symposium on, 2008, pp. 688-691.
P. Alshuth, et al., "Iris-image retrieval for images and videos," 1996.
G. P. Babu, et al., "Color indexing for efficient image retrieval," Multimedia Tools and Applications, vol. 1, pp. 327-348, 1995.
J. R. Bach, et al., "Virage image search engine: an open framework for image management," in Electronic Imaging: Science & Technology, 1996, pp. 76-87.
L. Bao and J.-c. Zeng, "Comparison and analysis of the selection mechanism in the artificial bee colony algorithm," in Hybrid Intelligent Systems, 2009. HIS'09. Ninth International Conference on, 2009, pp. 411-416.
M. BEN?O and R. Hudec, "Novel method for color textures features extraction based on GLCM," Radioengineering, vol. 4, pp. 64-67, 2007.
C. Chidambaram and H. S. Lopes, "A new approach for template matching in digital images using an artificial bee colony algorithm," in Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, 2009, pp. 146-151.
I. Daubechies, Ten lectures on wavelets vol. 61: SIAM, 1992.
B. Dinakaran, et al., "Interactive image retrieval using text and image content," Cybern Inf Tech, vol. 10, pp. 20-30, 2010.
G. Duan, et al., "Content-Based Image Retrieval Research," Physics Procedia, vol. 22, pp. 471-477, 2011.
M. El-gayar and H. Soliman, "A comparative study of image low level feature extraction algorithms," Egyptian Informatics Journal, vol. 14, pp. 175-181, 2013.
M. Flickner, et al., "Query by image and video content: The QBIC system," Computer, vol. 28, pp. 23-32, 1995.
R. Gonzalez and R. Woods, "Digital Image Processing," Addision-Wesley,Reading MA, 1992.
E. Hancer, et al., "Artificial Bee Colony based image clustering method," in Evolutionary Computation (CEC), 2012 IEEE Congress on, 2012, pp. 1-5.
Z.-C. Huang, et al., "Content-based image retrieval using color moment and Gabor texture feature," in Machine Learning and Cybernetics (ICMLC), 2010 International Conference on, 2010, pp. 719-724.
H. Jiawei and M. Kamber, "Data mining: concepts and techniques," San Francisco, CA, itd: Morgan Kaufmann, vol. 5, 2001.
S. Shefalli and B. Jindal, "Enhancing Content-based Image Retrieval using Moving K-Means Clustering Algorithm", International Journal of Computer Applications, vol. 102, no. 9, pp. 31-37, 2014.
L. Jinxia and Q. Yuehong, "Application of SIFT feature extraction algorithm on the image registration," in Electronic Measurement & Instruments (ICEMI), 2011 10th International Conference on, 2011, pp. 177-180.
A. Kannan, et al., "Image clustering and retrieval using image mining techniques," IEEE International Conference on Computational Intelligence and Computing Research, vol. 2, 2010.
D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Technical report-tr06, Erciyes university, engineering faculty, computer engineering department2005.
D. Karaboga and B. Basturk, "Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems," in Foundations of Fuzzy Logic and Soft Computing, ed: Springer, 2007, pp. 789-798.
D. Karaboga and C. Ozturk, "A novel clustering approach: Artificial Bee Colony (ABC) algorithm," Applied soft computing, vol. 11, pp. 652-657, 2011.
D. A. Karaboga, "Artificial bee colony (ABC) algorithm on training artificial neural networks," Signal Processing, 2007.
S. Krishnamachari and M. Abdel-Mottaleb, "Hierarchical clustering algorithm for fast image retrieval," in Electronic Imaging'99, 1998, pp. 427-435.
E. Lehmann, "Nonparametric Statistical Methods Based on Ranks," ed: San Francisco, CA: Holden-Day, 1975.
W.-Y. Ma and B. S. Manjunath, "Netra: A toolbox for navigating large image databases," Multimedia systems, vol. 7, pp. 184-198, 1999.
B. S. Manjunath and W.-Y. Ma, "Texture features for browsing and retrieval of image data," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 18, pp. 837-842, 1996.
H. B. Mann and D. R. Whitney, "On a test of whether one of two random variables is stochastically larger than the other," The annals of mathematical statistics, pp. 50-60, 1947.
M. Masotti, "A ranklet-based image representation for mass classification in digital mammograms," Medical physics, vol. 33, pp. 3951-3961, 2006.
A. Pentland, et al., "Photobook: Content-based manipulation of image databases," International journal of computer vision, vol. 18, pp. 233-254, 1996.
B. E. Prasad, et al., "A microcomputer-based image database management system," Industrial Electronics, IEEE Transactions on, pp. 83-88, 1987.
P. Reddy and K. S. Prasad, "Color and texture features for content based image retrieval," International Journal of Computer Technology Application, vol. 2, pp. 1016-1020, 2011.
E. Rosten and T. Drummond, "Fusing points and lines for high performance tracking," in Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, 2005, pp. 1508-1515.
Y. Rui, et al, "Image Retrieval: Current Techniques, Promising Directions, and Open Issues", Journal of Visual Communication and Image Representation, vol. 10, no. 1, pp. 39-62, 1999.
G. Serban and G. S. Moldovan, "A comparison of clustering techniques in aspect mining," Informatica, vol. 51, 2006.
S. A. Shafer, "Texture Representations for Image Retrieval," The George Washington University, 2012.
P. Vora and B. Oza, "A survey on k-mean clustering and particle swarm optimization," Int. J. Sci. Modern Eng, vol. 1, pp. 24-26, 2013.
J. R. Smith and S.-F. Chang, "VisualSEEk: a fully automated content-based image query system," in Proceedings of the fourth ACM international conference on Multimedia, 1997, pp. 87-98.
C. Solomon and T. Breckon, Fundamentals of Digital Image Processing: A practical approach with examples in Matlab: John Wiley & Sons, 2011.
M. J. Swain and D. H. Ballard, "Color indexing," International journal of computer vision, vol. 7, pp. 11-32, 1991.
B. Thomee and M. S. Lew, "Interactive search in image retrieval: a survey," International Journal of Multimedia Information Retrieval, vol. 1, pp. 71-86, 2012.
B. K. Verma and D. Kumar, "A review on Artificial Bee Colony algorithm," International Journal of Engineering & Technology, vol. 2, pp. 175-186, 2013.
X.-Y. Wang, et al., "Content-based image retrieval by integrating color and texture features," Multimedia Tools and Applications, vol. 68, pp. 545-569, 2014.
J.-K. Wu, et al., "CORE: a content-based retrieval engine for multimedia information systems," Multimedia systems, vol. 3, pp. 25-41, 1995.
B. Xue and L. Wanjun, "Research of image retrieval based on color," in Computer Science-Technology and Applications, 2009. IFCSTA'09. International Forum on, 2009, pp. 283-286.
Z. S. Younus, et al., "Content-based image retrieval using PSO and k-means clustering algorithm", Arabian Journal of Geosciences, vol. 8, no. 8, pp. 6211-6224, 2014.
R. Zabih and J. Woodfill, "Non-parametric local transforms for computing visual correspondence," in Computer Vision
D. Zhang, et al., "Content-based image retrieval using Gabor texture features," in IEEE Pacific-Rim Conference on Multimedia, University of Sydney, Australia, 2000.
M. Masotti and R. Campanini, "Texture classification using invariant ranklet features," Pattern Recognition Letters, vol. 29, pp. 1980-1986, 2008.
Y.-H. Lee, et al., "Efficient Image Retrieval Using Advanced Clustering SURF," in Network-Based Information Systems (NBiS), 2012 15th International Conference on, 2012, pp. 749-753
J. Zhang, et al., "A color indexing scheme using two-Level clustering processing for effective and efficient image retrieval," in Proceedings of the 2005 International Conference on Data Mining (DMIN'05), 2005, pp. 73-78.
X. Li, et al., "An effective content-based visual image retrieval system," in Computer Software and Applications Conference, 2002. COMPSAC 2002. Proceedings. 26th Annual International, 2002, pp. 914-919.
F. Smeraldi, "Ranklets: orientation selective non-parametric features applied to face detection," in Pattern Recognition, 2002. Proceedings. 16th International Conference on, 2002, pp. 379-382.
A. Del Bue, et al., "Non-rigid structure from motion using ranklet-based tracking and non-linear optimization," Image and Vision Computing, vol. 25, pp. 297-310, 2007.
M. A. Stricker and M. Orengo, "Similarity of color images," in IS&T/SPIE's Symposium on Electronic Imaging: Science & Technology, 1995, pp. 381-392.
C. Ozturk, et al., "Improved clustering criterion for image clustering with artificial bee colony algorithm," Pattern Analysis and Applications, pp. 1-13, 2014.
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