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

Autor:	M. V. Sudhamani, C. R. Venugopal
Jazyk:	angličtina
Rok vydání:	2007
Předmět:	Segmentation Computer Science::Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image Retrieval Features Clustering
DOI:	10.5281/zenodo.1061042
Popis:	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. {"references":["Y. Cheng, Mean shift, mode seeking, and clustering, IEEE Trans.\nPattern Anal. Machine Intell., vol. 17, 790-799, 1995.","J.-M. Jolion, P. Meer, S. Bataouche, Robust clustering with applications\nin computer vision, IEEE Trans. Pattern Anal. Machine Intell vol. 13,\n791-802, 1991.","W. Skarbek, A. Koschan, Colour Image Segmentation: A Survey,\nTechnical Report, Technical University Berlin, October 1994.","Dorin Comaniciu Peter Meer, Robust Analysis of Feature Spaces: Color\nImage Segmentation, Proc. IEEE Conference on Computer Vision and\nPattern Recognition, San Juan, Puerto Rico, pp. 750-755, June 1997.","Arnaldo J. Abrantes and Jorge S. Marques, The Mean Shift Algorithm\nand the Unifie Framework , Proceedings of the 17th International\nConference on Pattern Recognition -2004 (ICPR-04).","B. Georgescu, I. Shimshoni and P. Meer, Mean shift based clustering in\nhigh dimensions: A Texture classification example, Proc. Ninth IEEE\nInternational Conference on Computer Vision, pp. 456-463, Oct. 2003.","Peter Meer, Gerard Medioni and Sing Bing Kang, Robust techniques for\ncomputer vision (Prentice Hall, 2004).","Dorin Comaniciu and Peter Meer, Mean Shift Analysis and\nApplications,7th Int'l Conf. on Comp. Vis., Kerkyra, Greece, 1197-1203,\nSep. 1999.","Dorin Comaniciu and Visvanathan Ramesh ,Real-Time Tracking of\nNon-Rigid Objects using Mean Shift, IEEE CVPR, 2000.\n[10] Jeff Strickrott, A Survey of Image Segmentation Techniques for contentbased\nretrieval of multimedia data, Department of Computer Science,\nFlorida International University.\n[11] R. Sedgewick. Algorithms in C. Addison-Wesley, pp.441-449, 1990.\n[12] James W.wang, Integrated Region-Based Image Retrieval, Kluwer\nacademic publishers, 2001\n[13] Richard O.Duda, peter E. Hart, David G. stock, Pattern classification,\nwiley, 2002,\n[14] Forsyth and Ponce, A Computer Vision. A modren Approach, Prentice\nHall, 2003.\n[15] Werner Bailera, Peter Schallauera, Harald Bergur Haraldssonb, Herwig\nRehatscheka, Optimized Mean Shift Algorithm for Color Segmentation\nin Image Sequences, Proc. Conference on Image and Vid\nCommunications and Processing, IS&T/SPIE Electronic Imaging, San\nJose, CA, USA, Jan. 2005.\n[16] S.C Zhu and A.Yuille , Region competition: Unifying Snakes, Region\nGrowing, and Bayes/MDL for multiband Image Segmentation, IEEE\nTrans. Pattern analysis and Machine Intelligence, Vol. 18, no.9,\npp.884-900, Sept. 1996.\n[17] C. Wren, Azarbayejani, T. Darrell, and A. Pentland, pfinder: Real_Time\nTracking of the Human Body, IEEE trans. Pattern Analysis and\nMachine Intelligence, Vol. 19, no.7, pp.780-785, July 1997.\n[18] M. Tabb and N. Ahuja, Multiscale Image Segmentation by Integrated\nEdge and region Detection, IEEE Trans. Image Processing, vol. 6,\npp.642-655, 1997.\n[19] E.J. Pauwels and G.Frederix., Finding Salient Regions in Images,\nComputer vision and Image Understanding , vol. 75, pp. 73-85,1999.\n[20] A.K .Jain , R.P.W. Duin, and J.Mao, Statistical Pattern Recognition: A\nReview, IEEE Trans. Pattern Analysis and Machine Intelligence,\nvol.22, no.1, pp. 4-37, Jan 2000.\n[21] Y. Ohta, T.Kanade, and T.Sakai, Color Information for Region\nSegmentation, Compute Graphics and Image Processing, vol.13,\npp.222-241, 1980."]}
Databáze:	OpenAIRE
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