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Content Based Image Retrieval Based on Color, Texture and Shape Features Using Image and its Complement
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International Journal of Computer Science and Security (IJCSS)
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Volume:  1    Issue:  4
Pages:  1-47
Publication Date:   December 2007
ISSN (Online): 1985-1553
Pages 
25 - 35
Author(s)  
 
Published Date   
15-12-2007 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Multiresolution grid, Integrated matching, Conditional co-occurrence histograms, Local descriptors, Gradient vector flow field 
 
 
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Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. This paper presents a novel framework for combining all the three i.e. color, texture and shape information, and achieve higher retrieval efficiency using image and its complement. The image and its complement are partitioned into non-overlapping tiles of equal size. The features drawn from conditional co-occurrence histograms between the image tiles and corresponding complement tiles, in RGB color space, serve as local descriptors of color and texture. This local information is captured for two resolutions and two grid layouts that provide different details of the same image. An integrated matching scheme, based on most similar highest priority (MSHP) principle and the adjacency matrix of a bipartite graph formed using the tiles of query and target image, is provided for matching the images. Shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the color and texture features between image and its complement in conjunction with the shape features provide a robust feature set for image retrieval. The experimental results demonstrate the efficacy of the method. 
 
 
 
1 Ritendra Datta, Dhiraj Joshi, Jia Li and James Wang, “Image Retrieval: Ideas, Influences, and Trends of the New Age”, Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, November 10-11, 2005, Hilton, Singapore.
2 C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying,” in IEEE Trans. On PAMI, vol. 24, No.8, pp. 1026-1038, 2002.
3 Y. Chen and J. Z. Wang, “A Region-Based Fuzzy Feature Matching Approach to Content- Based Image Retrieval,” in IEEE Trans. on PAMI, vol. 24, No.9, pp. 1252-1267, 2002.
4 A. Natsev, R. Rastogi, and K. Shim, “WALRUS: A Similarity Retrieval Algorithm for Image Databases,” in Proc. ACM SIGMOD Int. Conf. Management of Data, pp. 395–406, 1999.
5 J. Li, J.Z. Wang, and G. Wiederhold, “IRM: Integrated Region Matching for Image Retrieval,” in Proc. of the 8th ACM Int. Conf. on Multimedia, pp. 147-156, Oct. 2000.
6 V. Mezaris, I. Kompatsiaris, and M. G. Strintzis, “Region-based Image Retrieval Using an Object Ontology and Relevance Feedback,” in Eurasip Journal on Applied Signal Processing, vol. 2004, No. 6, pp. 886-901, 2004.
7 W.Y. Ma and B.S. Manjunath, “NETRA: A Toolbox for Navigating Large Image Databases,” in Proc. IEEE Int. Conf. on Image Processing, vol. I, Santa Barbara, CA, pp. 568–571, Oct. 1997.
8 W. Niblack et al., “The QBIC Project: Querying Images by Content Using Color, Texture, and Shape,” in Proc. SPIE, vol. 1908, San Jose, CA, pp. 173–187, Feb. 1993.
9 A. Pentland, R. Picard, and S. Sclaroff, “Photobook: Content-based Manipulation of Image Databases,” in Proc. SPIE Storage and Retrieval for Image and Video Databases II, San Jose, CA, pp. 34–47, Feb. 1994.
10 10. M. Stricker, and M. Orengo, “Similarity of Color Images,” in Proc. SPIE Storage and Retrieval for Image and Video Databases, pp. 381-392, Feb. 1995.
11 http://wang.ist.psu.edu/
12 P.S.Hiremath, Jagadeesh Pujari, “Enhancing performance of region based image retrieval system using joint co-occurrence histograms between image and its complement in RGB color space.” in Proc. National Conference on Knowledge-Based computing systems and Frontier Technologies (NCKBFT-07), Manipal, India, 19-20 Feb, 2007.
13 Chenyang Xu, Jerry L Prince, “Snakes,Shapes, and Gradient Vector Flow”, IEEE Transactions on Image Processing, Vol-7, No 3,PP 359-369, March 1998.
14 T. Gevers and A.W.M. Smeuiders., “Combining color and shape invariant features for image retrieval”, Image and Vision computing, vol.17(7),pp. 475-488 , 1999.
15 A.K.Jain and Vailalya,, “Image retrieval using color and shape”, pattern recognition, vol. 29, pp. 1233-1244, 1996.
16 D.Lowe, “Distinctive image features from scale invariant keypoints”, International Journal of Computer vision, vol. 2(6),pp.91-110,2004.
17 K.Mikolajezyk and C.Schmid, “Scale and affine invariant interest point detectors”, International Journal of Computer Vision, vol. 1(60),pp. 63-86, 2004.
18 Etinne Loupias and Nieu Sebe, “Wavelet-based salient points: Applications to image retrieval using color and texture features”, in Advances in visual Information systems, Proceedings of the 4th International Conference, VISUAL 2000, pp. 223-232, 2000.
19 C. Harris and M. Stephens, “A combined corner and edge detectors”, 4th Alvey Vision Conference, pp. 147-151, 1988.
20 M.Banerjee, M,K,Kundu and P.K.Das, “Image Retrieval with Visually Prominent Features using Fuzzy set theoretic Evaluation”, ICVGIP 2004, India, Dec 2004.
21 Y. Rubner, L.J. Guibas, and C. Tomasi, “The earth mover’s distance, multi-dimensional scaling, and color-based image retrieval”, Proceedings of DARPA Image understanding Workshop, pp. 661-668, 1997.
22 D.Hoiem, R. Sukhtankar, H. Schneiderman, and L.Huston, “Object-Based Image retrieval Using Statistical structure of images”, Proc CVPR, 2004.
23 P. Howarth and S. Ruger, “Robust texture features for still-image retrieval”, IEE. Proceedings of Visual Image Signal Processing, Vol. 152, No. 6, December 2005.
24 Dengsheng Zhang, Guojun Lu, “Review of shape representation and description techniques”, Pattern Recognition Vol. 37,pp 1-19, 2004.
25 M. Sonka, V. Halvac, R.Boyle, Image Processing, Analysis and Machine Vision, Chapman & Hall, London, UK, NJ, 1993.
26 P.Nagabhushan, R. Pradeep Kumar, “Multiresolution Knowledge Mining using Wavelet Transform”, Proceeding of the International Conference on Cognition and Recognition, Mandya, pp781-792, Dec 2005.
 
 
 
1 S.R.Surya and G.Sasikala. “An Enhanced Image Retrieval using Contribution-Based Clustering Algorithm with Spatial Feature of Texture Primitive and Edge Detection”. International Journal of Computer Applications 33(2), pp.12-16, November 2011.
2 M. K. Shan, “Discovering Color Styles from Fine Art Images of Impressionism”, International Journal of Computer Science and Security (IJCSS), 3(4), pp. 314 – 324, 2009.
3 A. Kadir, L. E. Nugroho, A. Susanto and P. I. Santosa, “Leaf Classification Using Shape, Color, and Texture Features”, International Journal of Computer Trends and Technology, 1(3), pp. 225-230, 2011.
4 P. L. E. Ekmobo , M. Oumsis and M. Meknassi , “Motion Tracking in MRI by Harmonic State Model: Case of Heart Left Ventricle”, International Journal of Computer Science and Security (IJCSS), 3(5), pp. 428 – 447, 2009.
5 J. Mishra, A. Sharma and K. Chaturvedi, “An Unsupervised Cluster-based Image Retrieval Algorithm Using Relevance Feedback”, International Journal of Managing Information Technology (IJMIT), 3(2), pp. 9-16, 2011.
6 R. S. Mente, B. V. Dhandra and G. Mukarambi, “Colour Based Information Retrieval”, Int Jr of Advanced Computer Engineering and Architecture, 1(1), pp. 63-71, 2011.
7 X. Yang, Z. Wang, D. Li, J. Zhang; “Color image retrieval with adaptive feature weight in Brushlet domain ”, Presented at Web Society (SWS), 2010 IEEE 2nd Symposium , Beijing, pp. 97 – 102, 16-17 Aug. 2010.
8 I. Hastuti, M. Hariadi and I K. E. Purnama (2011) “Content Based Image Retrieval Berdasarkan Fitur Bentuk Menggunakan Metode Gradient Vector Flow Snake”. Presented at Seminar Nasional Informatika 2009 (semnasIF 2009) , UPN ”Veteran” Yogyakarta, 23 Mei 2009 , Telematika (17). pp. A140-A145
 
 
 
1 Gulbarga University
 
2 yasni
 
3 biblioteca universia de recursos
 
4 Baidu
 
 
 
P. S. Hiremath : Colleagues
Jagadeesh Pujari : Colleagues  
 
 
 
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