Segmentation of Images through Clustering to Extract Color Features: An Application forImage Retrieval

Authors: M. V. Sudhamani, C. R. Venugopal

Abstract:

This paper deals with the application for contentbased image retrieval to extract color feature from natural images stored in the image database by segmenting the image through clustering. We employ a class of nonparametric techniques in which the data points are regarded as samples from an unknown probability density. Explicit computation of the density is avoided by using the mean shift procedure, a robust clustering technique, which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters. A non-parametric technique for the recovery of significant image features is presented and segmentation module is developed using the mean shift algorithm to segment each image. In these algorithms, the only user set parameter is the resolution of the analysis and either gray level or color images are accepted as inputs. Extensive experimental results illustrate excellent performance.

Keywords: Segmentation, Clustering, Image Retrieval, Features.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061042

References:

[1] Y. Cheng, Mean shift, mode seeking, and clustering, IEEE Trans. Pattern Anal. Machine Intell., vol. 17, 790-799, 1995.
[2] J.-M. Jolion, P. Meer, S. Bataouche, Robust clustering with applications in computer vision, IEEE Trans. Pattern Anal. Machine Intell vol. 13, 791-802, 1991.
[3] W. Skarbek, A. Koschan, Colour Image Segmentation: A Survey, Technical Report, Technical University Berlin, October 1994.
[4] Dorin Comaniciu Peter Meer, Robust Analysis of Feature Spaces: Color Image Segmentation, Proc. IEEE Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp. 750-755, June 1997.
[5] Arnaldo J. Abrantes and Jorge S. Marques, The Mean Shift Algorithm and the Unifie Framework , Proceedings of the 17th International Conference on Pattern Recognition -2004 (ICPR-04).
[6] B. Georgescu, I. Shimshoni and P. Meer, Mean shift based clustering in high dimensions: A Texture classification example, Proc. Ninth IEEE International Conference on Computer Vision, pp. 456-463, Oct. 2003.
[7] Peter Meer, Gerard Medioni and Sing Bing Kang, Robust techniques for computer vision (Prentice Hall, 2004).
[8] Dorin Comaniciu and Peter Meer, Mean Shift Analysis and Applications,7th Int'l Conf. on Comp. Vis., Kerkyra, Greece, 1197-1203, Sep. 1999.
[9] Dorin Comaniciu and Visvanathan Ramesh ,Real-Time Tracking of Non-Rigid Objects using Mean Shift, IEEE CVPR, 2000.
[10] Jeff Strickrott, A Survey of Image Segmentation Techniques for contentbased retrieval of multimedia data, Department of Computer Science, Florida International University.
[11] R. Sedgewick. Algorithms in C. Addison-Wesley, pp.441-449, 1990.
[12] James W.wang, Integrated Region-Based Image Retrieval, Kluwer academic publishers, 2001
[13] Richard O.Duda, peter E. Hart, David G. stock, Pattern classification, wiley, 2002,
[14] Forsyth and Ponce, A Computer Vision. A modren Approach, Prentice Hall, 2003.
[15] Werner Bailera, Peter Schallauera, Harald Bergur Haraldssonb, Herwig Rehatscheka, Optimized Mean Shift Algorithm for Color Segmentation in Image Sequences, Proc. Conference on Image and Vid Communications and Processing, IS&T/SPIE Electronic Imaging, San Jose, CA, USA, Jan. 2005.
[16] S.C Zhu and A.Yuille , Region competition: Unifying Snakes, Region Growing, and Bayes/MDL for multiband Image Segmentation, IEEE Trans. Pattern analysis and Machine Intelligence, Vol. 18, no.9, pp.884-900, Sept. 1996.
[17] C. Wren, Azarbayejani, T. Darrell, and A. Pentland, pfinder: Real_Time Tracking of the Human Body, IEEE trans. Pattern Analysis and Machine Intelligence, Vol. 19, no.7, pp.780-785, July 1997.
[18] M. Tabb and N. Ahuja, Multiscale Image Segmentation by Integrated Edge and region Detection, IEEE Trans. Image Processing, vol. 6, pp.642-655, 1997.
[19] E.J. Pauwels and G.Frederix., Finding Salient Regions in Images, Computer vision and Image Understanding , vol. 75, pp. 73-85,1999.
[20] A.K .Jain , R.P.W. Duin, and J.Mao, Statistical Pattern Recognition: A Review, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.22, no.1, pp. 4-37, Jan 2000.
[21] Y. Ohta, T.Kanade, and T.Sakai, Color Information for Region Segmentation, Compute Graphics and Image Processing, vol.13, pp.222-241, 1980.