Home   >   CSC-OpenAccess Library   >    Manuscript Information
Content Based Image Retrieval Based on Color, Texture and Shape Features Using Image and its Complement
P. S. Hiremath, Jagadeesh Pujari
Pages - 25 - 35     |    Revised - 15-12-2007     |    Published - 15-12-2007
Volume - 1   Issue - 4    |    Publication Date - December 2007  Table of Contents
Multiresolution grid, Integrated matching, Conditional co-occurrence histograms, Local descriptors, Gradient vector flow field
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.
CITED BY (4)  
1 Li, D., & Wang, Y. (2015). The Image Retrieval Based on the Hybrid Algorithm of the Primary Color and Color Layout Descriptor.
2 Vimina, E. R., & Jacob, K. P. (2014). An Evaluation of Image Matching Algorithms for Region Based Image Retrieval. International Journal of Advancements in Computing Technology, 6(6), 75.
3 Sun, D., Deng, H., Wang, F., Ji, K., Dai, W., Liang, B., & Wei, S. (2013, November). The Feature Related Techniques in Content-Based Image Retrieval and Their Application in Solar Image Data. In Intelligent Networks and Intelligent Systems (ICINIS), 2013 6th International Conference on (pp. 336-339). IEEE.
4 Jalab, H. A. (2011, September). Image retrieval system based on color layout descriptor and Gabor filters. In Open Systems (ICOS), 2011 IEEE Conference on (pp. 32-36). IEEE.
1 Google Scholar 
2 Academic Journals Database 
3 ScientificCommons 
4 Academic Index 
5 CiteSeerX 
6 refSeek 
7 iSEEK 
8 ResearchGATE 
9 Libsearch 
10 Bielefeld Academic Search Engine (BASE) 
11 Scribd 
12 WorldCat 
13 SlideShare 
15 PdfSR 
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.
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.
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.
A.K.Jain and Vailalya,, “Image retrieval using color and shape”, pattern recognition, vol. 29, pp. 1233-1244, 1996.
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.
C. Harris and M. Stephens, “A combined corner and edge detectors”, 4th Alvey Vision Conference, pp. 147-151, 1988.
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.
D.Hoiem, R. Sukhtankar, H. Schneiderman, and L.Huston, “Object-Based Image retrieval Using Statistical structure of images”, Proc CVPR, 2004.
D.Lowe, “Distinctive image features from scale invariant keypoints”, International Journal of Computer vision, vol. 2(6),pp.91-110,2004.
Dengsheng Zhang, Guojun Lu, “Review of shape representation and description techniques”, Pattern Recognition Vol. 37,pp 1-19, 2004.
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.
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.
K.Mikolajezyk and C.Schmid, “Scale and affine invariant interest point detectors”, International Journal of Computer Vision, vol. 1(60),pp. 63-86, 2004.
M. Sonka, V. Halvac, R.Boyle, Image Processing, Analysis and Machine Vision, Chapman & Hall, London, UK, NJ, 1993.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Mr. P. S. Hiremath
- India
Mr. Jagadeesh Pujari
- India