Segmentation of Images through Clustering to Extract Color Features: An Application forImage Retrieval
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:54529", author = "M. V. Sudhamani and C. R. Venugopal", title = "Segmentation of Images through Clustering to Extract Color Features: An Application forImage Retrieval", 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.", volume = "1", number = "8", pages = "2429-8", }
