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Coefficient of Correlation Based CBIR

Neetesh Gupta, Niket Bhargava, Md. Ilyas Khan, Shiv Kumar, Dr. Bhupendra Verma

Abstract


For the purpose of content-based image retrieval (CBIR) An up-to-date comparison of state-of-the-art low-level color and texture feature extraction approach is presented in this paper. The CBIR problem is identified by us because there is a need to search the huge databases having images efficiently and effectively. in this paper we suggest a color and texture feature extraction algorithms. Special attention is given for CBIR is the similarity measurement using correlation coefficient with distinct distance matrices properties. A New approach for image retrieval technique is proposed to improve retrieval performance, and reduce the extraction search times. Matching is performed between the test image and the object image and quality of matching is measured in terms of grading.


Keywords


Similarity, Measurement, CBIR, Low Level Feature Extraction, Correlation Coefficient.

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References


K.P. Ajitha Gladis, K.Ramar, “A Novel Method for Content Based Image Retrieval Using the Approximation of Statistical Features, Morphological Features and BPN Network”, IEEE computer society ICCIMA 2007 ,Vol. 148 , PP. 179-184

Tienwei Tsai, Te-Wei Chiang, and Yo-Ping Huan, “Image Retrieval Approach Using Distance, Threshold Pruning”, IEEE Trans. On Image Processing 2007, Vol.12,PP.241-249.

S. Newsam and C. Kamath, “Comparing shape and texture features for pattern recognition in simulation data,” in SPIE Electronic Imaging, San Jose,CA, January 2005, pp. 106–117.

ChengjunLiu,”ABayesian Discriminating Features Method for Face Detection”, IEEETrans PAMI, Vol. 25, No. 6, June 2003.

Feature Histogram For Content Based Retreival. zur Erlangung des Doktorgrades , 2002.

Carson, S. Belongie, H. Greenspan, J. Malik, “Blobworld Image segmentation using Expectation Maximization and its Applications to Querying Images” IEEE Trans PAMI, Vol. 24, No. 8, 2002

A. Adjeroh and M. C. Lee, “On ratio-based color indexing,” IEEE Trans.Image Processing, , vol. 10, no. 1, pp. 36– 48, 2001

A.A.Goodrum, “Image Information retrieval” : An overview of current research, vol. 3, no. 2, 2000

K. Hachimura and A. Tojima. Image retrieval based on compositional featureand interactive query specification. In IAPR International Conference on PatterRecognition (ICPR), volume 4, pages 262–266, Barcelona, Spain, September 2000

M. K. Mandal, F. Idris, and S. Panchanathan, “A critical evaluation of image and video indexing techniques in the compressed domain,” Image and Vision Computing, vol. 17, no. 7, pp. 513–529, May 1998.

Muller, H., Muller, W., Squire, D.M., Marchand-Maillet, S., Pun, T. “ Performance evaluation in content-based image retrieval: overview and proposals” Pattern Recognition Letters 22 (2001) 593–601

Lindberg. “Feature detection with automatic scale selection.” International Journal of Computer Vision, 30(2):79 116, 1998.

Y. Rui, T. S. Huang and S. Chang, “Image Retrieval: Current Techniques, Promising Directions and Open Issues”, Journal of Visual Communication and Image Representation, vol. 10, pp. 39-62, March 1999.

W. Y. Ma and B. S. Manjunath, “ Texture features and learning similarity ”, in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1996, pp. 425–430.

J. Vogel and B. Schiele, “ On Performance Characterization and Optimization for Image Retrieval”, 7th European Conference on Computer Vision, Springer, 49-63 (2002).

P. Duygulu, K. Barnard, J. F. G. D. Freitas, and D. A. Forsyth, “Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary”, The Seventh European Conference on Computer Vision, IV:97-112 (2002).

J. Shotton, M. Johnson, and R. Cipolla. “ Semantic texton forests for image categorization and segmentation.” In Proc. of IEEE CVPR, 2008. Accepted.

Ville Viitaniemi and Jorma Laaksonen. “ Techniques for still image scene lassification and object detection” In Proc. of ICANN 2006, volume 2, pages 35–44, Athens, Greece, September 2006. Springer

Low level color and texture feature extraction for content based image retrieval EE 381k Multi Dimensional Digital Signal Processing Michele Saad May 09,2008


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