IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 12    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 12, December 2011
Natural Image Segmentation and Object Recognition using ACA and Steerable Filter based on Color-Texture Features
Full Text(PDF, 3000)  PP.  
Mrs. Nilima Kulkarni, Dr. Mrs. A.M. Rajurkar
Adaptive clustering algorithm (ACA), content based image retrieval (CBIR), color values, human visual system (HVS), local median energy, natural images, optimal color composition distance (OCCD), steerable filter decomposition
An image segmentation algorithm that is based on low-level features for color and texture is presented. It is aimed at segmentation of natural scenes, in which the color and texture of each segment does not typically exhibit uniform statistical characteristics. The proposed approach combines knowledge of human perception with an understanding of signal characteristics in order to segment natural scenes into perceptually/semantically uniform regions. The proposed approach is based on two types of spatially adaptive low-level features. The first describes the local color composition in terms of spatially adaptive dominant colors, and the second describes the spatial characteristics of the grayscale component of the texture. Together, they provide a simple and effective characterization of texture that the proposed algorithm uses to obtain robust and, at the same time, accurate and precise segmentations. We have tried to recognize objects in images. We used color values for object recognition. The images are assumed to be of relatively low resolution and may be degraded compressed.
[1] Y. Rui, T. S. Huang, and S.-F. Chang, “Image retrieval: Current tech- niques, promising directions and open issues,” J. Vis. Commun. Image Represen., vol. 10, pp. 39–62, Mar. 1999.

[2] W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Con- tent-based image retrieval at the end of the early years,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349–1379, Dec. 2000.

[3] A. Kundu and J.-L. Chen, “Texture classification using QMF bank-based subband decomposition,” Comput. Vis., Graphics, and Image Process , Graph. Models Image Process., vol. 54, pp. 369–384, Sep. 1992.

[4] T. Chang and C.-C. J. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process., vol. 2, no. 10, pp. 429–441, Oct. 1993.

[5] M. Unser, “Texture classification and segmentation using wavelet frames,” IEEE Trans. Image Process., vol. 4, no. 11, pp. 1549–1560,Nov. 1995.

[6] T. Randen and J. H. Husoy, “Texture segmentation using filters with optimized energy separation,” IEEE Trans. Image Process., vol. 8, no. 4, pp. 571–582, Apr. 1999.

[7] G. V. de Wouwer, P. Scheunders, and D. Van Dyck, “Statistical texture characterization from discrete wavelet representations,” IEEE Trans. Image Process., vol. 8, no. 4, pp. 592–598, Apr. 1999.

[8] T. N. Pappas, “An adaptive clustering algorithm for image segmenta- tion,” IEEE Trans. Signal Process., vol. SP-40, no. 4, pp. 901–914, Apr.1992.

[9] M. M. Chang, M. I. Sezan, and A. M. Tekalp, “Adaptive Bayesian seg- mentation of color images,” J. Electron. Imag., vol. 3, pp. 404–414, Oct.1994.

[10] D. Comaniciu and P. Meer, “Robust analysis of feature spaces: Color image segmentation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Juan, PR, Jun. 1997, pp. 750–755.

[11] J. Luo, R. T. Gray, and H.-C. Lee, “Incorporation of derivative priors in adaptive Bayesian color image segmentation,” in Proc. Int. Conf. Image Processing, vol. III, Chicago, IL, Oct. 1998, pp. 780–784.

[12] Y. Deng and B. S. Manjunath, “Unsupervised segmentation of color- texture regions in images and video,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 8, pp. 800–810, Aug. 2001.

[13] S. Belongie, C. Carson, H. Greenspan, and J. Malik, “Color- and texture- based image segmentation using EM and its application to content based image retrieval,” in Proc. ICCV, 1998, pp. 675–682.

[14] D. K. Panjwani and G. Healey, “Markov random-field models for unsu- pervised segmentation of textured color images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 10, pp. 939–954, Oct. 1995.

[15] J. Wang, “Stochastic relaxation on partitions with connected components and its application to image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 6, pp. 619–636, Jun. 1998.

[16] L. Shafarenko, M. Petrou, and J. Kittler, “Automatic watershed segmen- tation of randomly textured color images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 6, no. 11, pp. 1530–1544, Nov. 1997.

[17] W. Ma and B. S. Manjunath, “Edge flow: A technique for boundary de- tection and image segmentation,” IEEE Trans. Image Process., vol. 9, no. 8, pp. 1375–1388, Aug. 2000.

[18] J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888–905, Aug. 2000.

[19] A. Mojsilovic and B. Rogowitz, “Capturing image semantics with low- level descriptors,” in Proc. Int. Conf. Image Processing, Thessaloniki, Greece, Oct. 2001, pp. 18–21.

[20] J. Portilla and E. P. Simoncelli, “A parametric texture model based on joint statictics of complex wavelet coefficients,” Int. J. Comput. Vis., vol.40, pp. 49–71, Oct. 2000.

[21] A. Mojsilovic´, J. Kovacˇevic´, J. Hu, R. J. Safranek, and S. K. Ganapathy, “Matching and retrieval based on the vocabulary and grammar of color patterns,” IEEE Trans. Image Process., vol. 1, no. 1, pp. 38–54, Jan.2000.

[22] W. Y. Ma, Y. Deng, and B. S. Manjunath, “Tools for texture/color based search of images,” in Proc. SPIE Human Vision and Electronic ImagingII, vol. 3016, B. E. Rogowitz and T. N. Pappas, Eds., San Jose, CA, 1997, pp. 496–507

[23] A. Mojsilovic´, J. Hu, and E. Soljanin, “Extraction of perceptually important colors and similarity measurement for image matching, retrieval, and analysis,” IEEE Trans. Image Process., vol. 11, no. 11, pp. 1238–1248, Nov. 2002.

[24] M. Swain and D. Ballard, “Color indexing,” Int. J. Comput. Vis., vol. 7, no. 1, pp. 11–32, 1991.

[25] W. Niblack, R. Berber, W. Equitz, M. Flickner, E. Glaman, D. Petkovic, and P. Yanker, “The QBIC project: Quering images by content using color, texture, and shape,” in Proc. SPIE Storage and Retrieval for Image and Video Data Bases, vol. 1908, San Jose, CA, 1993, pp. 173–187.

[26] B. S. Manjunath, J.-R. Ohm, V. V. Vasudevan, and A. Yamada, “Color and texture descriptors,” IEEE Trans. Circuits Syst. Video Technol., vol.11, no. 6, pp. 703–715, Jun. 2001.

[27] Junquing Chan, T. N. Pappas, Aleksandra Mojsilovic and Bernice E. Rogowitz “Adaptive Perceptual Color-Texture Image Segmentation” IEEE Transaction on Image Processing VOL 14, NO. 10, October 2005

Untitled Page