A color image segmentation approach for content-based image retrieval

This paper describes a new color image segmentation method based on low-level features including color, texture and spatial information. The mean-shift algorithm with color and spatial information in color image segmentation is in general successful, however, in some cases, the color and spatial information are not sufficient for superior segmentation. The proposed method addresses this problem and employs texture descriptors as an additional feature. The method uses wavelet frames that provide translation invariant texture analysis. The method integrates additional texture feature to the color and spatial space of standard mean-shift segmentation algorithm. The new algorithm with high dimensional extended feature space provides better results than standard mean-shift segmentation algorithm as shown in experimental results.

[1]  Spyros Liapis,et al.  Color and texture image retrieval using chromaticity histograms and wavelet frames , 2004, IEEE Transactions on Multimedia.

[2]  Trygve Randen,et al.  Texture segmentation using filters with optimized energy separation , 1999, IEEE Trans. Image Process..

[3]  Jitendra Malik,et al.  Color- and texture-based image segmentation using EM and its application to content-based image retrieval , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[4]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  R. Casey,et al.  Advances in Pattern Recognition , 1971 .

[6]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Aleksandra Mojsilovic,et al.  Image segmentation by spatially adaptive color and texture features , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[8]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[9]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[10]  Josef Kittler,et al.  Automatic watershed segmentation of randomly textured color images , 1997, IEEE Trans. Image Process..

[11]  Jia-Ping Wang,et al.  Stochastic Relaxation on Partitions With Connected Components and Its Application to Image Segmentation , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Paul Scheunders,et al.  Statistical texture characterization from discrete wavelet representations , 1999, IEEE Trans. Image Process..

[13]  B. S. Manjunath,et al.  Unsupervised Segmentation of Color-Texture Regions in Images and Video , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Glenn Healey,et al.  Markov Random Field Models for Unsupervised Segmentation of Textured Color Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Michal Haindl,et al.  Unsupervised Texture Segmentation , 1998, SSPR/SPR.

[17]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..