Open Access Open Access  Restricted Access Subscription or Fee Access

An Efficient Image Clustering and Content Based Image Retrieval Using Fuzzy K Means Clustering Algorithm

(*) Corresponding author

Authors' affiliations



The construction of large database with thousands of data has been facilitated by the developments in data storage and image acquisition technologies. Suitable information system requires proper handling of these datasets in efficient manner. Content-Based Image Retrieval (CBIR) is commonly used system to handle these datasets. Basis on the image substance CBIR extracts the images that are relevant to the user given query image from large image databases. Many of the CBIR systems retrieval of the result are corresponding to feature similarities for user given query, ignoring the similarities among images in database. These existing CBIR system measures the feature similarities by using k means algorithm, but the traditional k-means algorithm mostly depends on the selection of initial centers values, the algorithm normally uses random procedures to get them and it degrades the performance of the CBIR retrieval results.  To overcome the problem of initial centroid random  selection process  in K means clustering algorithm use the fuzzy logic based feature similarities information with K means clustering algorithm to image retrieval system. Combining both low-level and high-level visual features, the fuzzy k means algorithm entirely measures the features similarities information between the images in larger dataset. Fuzzy k means clustering algorithm optimizes the relevance results from conventional image retrieval system by firstly clustering the related images in the images database to improve the effectiveness of images retrieval system.
Copyright © 2014 Praise Worthy Prize - All rights reserved.


Image Clustering; Fuzzy K Means; K Means Unsupervised Classification; Content Based Image Retrieval (CBIR)

Full Text:



Snehal Mahajan and Dharmaraj Patil “A survey on contribution based clustering algorithm with texture features” World Journal of Science and Technology 2012, 2(3):52-56 ISSN: 2231 – 2587.

Qi, Y., Li, Y., A high efficiency scheme of retrieval image with complex background, (2012) International Review on Computers and Software (IRECOS), 7 (6), pp. 3012-3014.

E. Zagrouba, S. Ouni, W. Barhoumi, A Reliable Image Retrieval System Based on Spatial Disposition Graph Matching, (2007) International Review on Computers and Software (IRECOS), 2. (2), pp. 108 - 117.

Niblack, C., and Barber, “The QBIC project: Querying images by content using color, texture and shape,” in proceedings of SPIE Storage and Retrieval for Image and Video Databases, San Jose, CA, vol. 1908, pp.173-187, February 1993.

Bach and Fuller, “The virage image search engine: An open framework for image management,” in proceedings of SPIE Conference on Storage and Retrieval for Image and Video Databases, San Jose, CA, vol. 4, pp. 76-87, February, 1996.

Pentland, Picard and Sclaroff, “Photobook: Content-based manipulation of image databases,” International Journal of Computer Vision, vol. 18, no. 3, pp. 233-254, June 1996.

Smith, J., and Chang, S., “Visualseek: A fully automated content-based image query system,” in proceedings of the fourth ACM international conference on Multimedia, Boston, Massachusetts, United States, pp. 87 - 98, November 18 - 22, 1996

Wong, Y., Hoi, S., and Lyu, M., "An Empirical Study on Large-Scale Content-Based Image Retrieval," in proceedings of IEEE International Conference on Multimedia and Expo, Beijing, pp. 2206-2209, 2-5 July, 2007.

Huang, J., Zia, A., Zhou, J., and Robles-Kelly, A., "Content-based Image Retrieval via Subspace-projected Salient Features," in proceedings of Digital Image Computing: Techniques and Applications, Canberra, ACT, pp. 593-599, 1-3 December, 2008.

S. M. Aji and R. J. McEliece. The Generalized Distributive Law. IEEE Transactions on Information Theory, 46(2):325–343, 2000.

Narasimhan and Ramraj. Jul 29 - Aug 01, 2010 Contribution- Based Clustering Algorithm for Content-Based Image.Retrieval .2010 5th International Conference on Industrial and InformationSystems.ICIIS 2010.India

Bradley Scaling clustering algorithms to large databases .in Proc. 4th Int. Conf. Knowledge Discovery andDataMining (KDD’98).pp. 9–15.

Shrivastava Comparison between K-Mean and CMean Clustering for CBIR. Second International Conference on Computational Intelligence, Modelling and Simulation.

Long Fundamentals of content-based image retrieval . Springer-Verlag, New York, pp. 1–26.

Trojacanec K., Dimitrovski I., and Loskovska S., "Content Based Image Retrieval in Medical Applications: An Improvement of the Two-Level Architecture," in Proceedings of IEEE Eurocon, Petersburg, pp. 118-121,2009.

Nandagorialan S., Adip B., and Deepak N., "A Universal Model for Content-Based Image Retrieval," World Academy of Science, Engineering and Technolo&T, vol. 46, pp. 644- 647,2008.

Alpkocak, A., Ozturkmenoglu, O., Berber, T., Vahid, A.H., Hamed, R.G.: DEMIR at ImageCLEFMed 2011: Evaluation of Fusion Techniques for Multimodal Content-based Medical Image Retrieval. 12th Workshop of the Cross-Language Evaluation Forum (CLEF), Amsterdam, Netherlands (2011).

Castillo J., Medina J., and Sanchez D., "A system to Perform CBIR on X-Ray Images using Soft Computing Techniques," in Proceedings of International Conference on Fuzzy Systems, Jetu Island, pp. 1314-1319, 2009.

Csurka, G., Clinchant, S., Jacquet, G.: XRCE’s Participation at Medical Image Modality Classification and Ad-hoc Retrieval Tasks of Image CLEF 2011 (2011).

Prasad, B. G., Biswas, K. K., Gupta, S. K.: Region-based image retrieval using integrated color, shape, and location index. Comput. Vis. Image Underst. Vol. 94, 193–233. (2004)

Chandra and K.Kanagalakshmi, Noise Elimination in Fingerprint Images using Median Filter, Int. Journal of Advanced Networking and Applications,(2011),Vol. 02, Issue:06, pp:950-955.

E.Chandra and K.Kanagalakshmi, Performance Evaluation of Filters in Noise Removal of Fingerprint Image, Proceedings of ICECT-2011, 3rd International Conference on Electronics and Computer Technology,(2011), vol. 1, pp. 117-123, ISBN: 978-1-4244-8677-9, Published by IEEE, Catalog no.: CFP1195F-PRT.

Poorani M1, Prathiba T2, Ravindran G3 “Integrated Feature Extraction for Image Retrieval” IJCSMC, Vol. 2, Issue. 2, February 2013, pg.28 – 35

Chih-tang chang1, jim z. C. Lai2 and mu-der jeng “A Fuzzy K-means Clustering Algorithm Using Cluster Center Displacement” Journal of Information Science And Engineering 27, 995-1009 (2011).

Deevena Raju, B., Pandarinath, P., Prasad, G.S., An efficient image reconstruction technique with aid of PSO (Particle Swarm Optimization) and DWT (Discrete Wavelet Transform), (2013) International Review on Computers and Software (IRECOS), 8 (8), pp. 1871-1877.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize