Open Access Open Access  Restricted Access Subscription or Fee Access

A Comparative Analysis of Clustering Algorithms for Content Based Image Retrieval

D. Napoleon, M. Praneesh, P. Ramya

Abstract


Content based image retrieval is a set of techniques
for retrieving semantically relevant images from an image data basedon automatically derived image features. In CBIR, Image are indexedby their visual content, such as color, texture and shapes. Furtherresearch has suggested that the usage of clustering technique ofimage retrieval. For this paper we compare Fuzzy Possiblistic CMeansclustering algorithm for retrieving the most similar images. Inour experimental results shows that the modify Fuzzy PossiblisticClustering Algorithm is better retrieval.


Keywords


Query, Modify Fuzzy Possiblistic C-Means, Content- based Image Retrieval

Full Text:

PDF

References


Li, J., Wang, J. Z. and Wiederhold, G.,―Integrated Region Matching for

ImageRetrieval,‖ ACM Multimedia, 2000, p. 147-156.

Flickner, M., Sawhney, H., Niblack, W.,Ashley, J., Huang, Q., Dom, B.,

Gorkani, M.,Hafner, J., Lee, D., Petkovic, D., Steele, D.and Yanker, P.,

―Query by image and videocontent: The QBIC system,‖ IEEE

Computer,28(9), 1995,pp.23-32

Pentland, A., Picard, R. and Sclaroff S.,―Photo book: Content based

manipulation of image databases‖, International Journal ofComputer

Vision, 18(3), 1996, pp.233–254

Smith, J.R., and Chang, S.F., ―Single color extraction and image query,‖

In Proceeding IEEE International Conference on ImageProcessing,

, pp. 528–531

Gupta, A., and Jain, R., ―Visual information retrieval,‖ Comm. Assoc.

Comp. Mach., 40(5), 1997, pp. 70–79.

M. Saadatmand-Tarzjan and H. A. Moghaddam, ―A Novel Evolutionary

Approach for Optimizing Content-Based Image Indexing Algorithms‖,

IEEE Transactions On Systems, Man, And Cybernetics—Part B:

Cybernetics, Vol. 37, No. 1, February 2007, pp. 139 153.

N. Vasconcelos, ―From Pixels to Semantic Spaces: Advances in

Content-Based ImageRetrieval‖,Computer Volume: 40, Issue: 7, 2007,

pp. 20-26.

N. Rasiwasia and N. Vasconcelos, ―A Study of Query by Semantic

Example‖, 3rd International Workshop on Semantic Learning and

Applications in Multimedia, Anchorage, June 2008, pp. 1-8.

N. Rasiwasia, P. J. Moreno and N. Vasconcelos, ―Bridging the Gap:

Query by Semantic Example‖, IEEE Transactions On Multimedia, Vol.

, No. 5, August 2007, pp. 923-938.

S. Cheng, W. Huang, Y. Liao and D. Wu, ―A Parallel CBIR

Implementation Using Perceptual Grouping Of Block-based Visual

Patterns‖, IEEE International Conference on Image Processing – ICIP,

, pp. V -161 - V - 164.

D. Tao, X. Tang, and X. Li ―Which Components are Important for

Interactive Image Searching?‖, IEEE Transactions On Circuits And

Systems For Video Technology, Vol. 18, No. 1, January 2008, pp. 3-11.

Datta, R., Li, J., Wang, J.Z.: Content-based image retrieval: approaches

and trends of the new age. In: MIR 2005: Proceedings of the 7th ACM

SIGMM international workshop on Multimedia information retrieval,

pp. 253–262. ACM Press, New York (2005)

Chang, H., Yeung, D.Y.: Kernel-based distance metric learning for

content-based image retrieval. Image Vision Comput. 25, 695–703

(2007)

Cz´uni, L., Csord´as, D.: Depth-based indexing and retrieval of

photographic images. In: Garc´ıa, N., Salgado, L., Mart´ınez, J.M. (eds.)

VLBV 2003.LNCS, vol. 2849, pp. 76–83. Springer, Heidelberg (2003)

Zhang, D.S., Lu, G.: A comparative study on shape retrieval using

fourier descriptors with different shape signatures. In: Proc. of

International Conference on Intelligent Multimedia and Distance

Education (ICIMADE 2001), Fargo, ND, USA, pp. 1–9 (2001)


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.