The week's pick
Random Articles
Reseach Article
CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 18 - Number 6 |
| Year of Publication: 2011 |
| Authors: M.Babu Rao, B.Prabhakara Rao, A.Govardhan |
10.5120/2285-2961
|
M.Babu Rao, B.Prabhakara Rao, A.Govardhan . CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features. International Journal of Computer Applications. 18, 6 ( March 2011), 40-46. DOI=10.5120/2285-2961
Abstract
There is a great need of developing efficient content based image retrieval systems because of the availability of large image databases. A new image retrieval system CTDCIRS (color-texture and dominant color based image retrieval system) to retrieve the images using three features called dynamic dominant color (DDC), Motif co-occurrence matrix (MCM) and difference between pixels of scan pattern (DBPSP) is proposed. Initially the image is divided into eight coarse partitions using the fast color quantization algorithm and the eight dominant colors are obtained from eight partitions. Next the texture of the image is represented by the MCM and DBPSP. MCM is derived using a motif transformed image. MCM is similar to color co-occurrence matrix (CCM). MCM is the conventional pattern co-occurrence matrix that calculates the probability of the occurrence of same pixel color between each pixel and its adjacent ones in each image, and this probability is considered as the attribute of the image.MCM captures third order image statistics in the local neighborhood which describes the direction of textures but not the complexity of the textures. That is why the DBPSP is also considered as one of the texture features. The three features Dominant color, MCM and DBPSP are integrated to facilitate the image retrieval system. Experimental results show that the proposed image retrieval is more efficient in retrieving the user- interested images.
References
- Ritendra Datta, Dhiraj Joshi, Jia Li, James Z. Wang, Image retrieval: ideas, influences, and trends of the new age, ACM Computing Surveys 40 (2) (2008) 1–60.
- Y. Rui, T.S. Huang, S.F. Chang, Image retrieval: current techniques, promising directions, and open issues, J. Visual Commun. Image Representation 10 (1999) 39–62.
- Xuelong Li, Image retrieval based on perceptive weighted color blocks, Pattern Recognition Letters 24 (12) (2003) 1935–1941.
- ISO/IEC 15938-3/FDIS Information Technology—Multimedia Content Description Interface—Part 3 Visual Jul. 2001, ISO/IEC/JTC1/SC29/WG11 Doc. N4358.
- L.M. Po, K.M. Wong, A new palette histogram similarity measure for MPEG-7 dominant color descriptor, Proceedings of the 2004 IEEE International Conference on Image Processing (ICIP'04), Singapore, October 2004, pp. 1533–1536.
- Rui Min, H.D. Cheng, Effective image retrieval using dominant color descriptor and fuzzy support vector machine, Pattern Recognition 42 (1) (2009) 147–157.
- Nai-Chung Yang, Wei-Han Chang, Chung-Ming Kuo, Tsia-Hsing Li, A fast MPEG-7 dominant color extraction with new similarity measure for image retrieval, Journal of Visual Communication and Image Representation 19 (2) (2008) 92–105.
- Tzu-Chuen Lu, Chin-Chen Chang, Color image retrieval technique based on color features and image bitmap, Information Processing and Management 43 (2) (2007) 461–472.
- P.W. Huang, S.K. Dai, Image retrieval by texture similarity, Pattern Recognition 36 (3) (2003) 665–679.
- I. Daubechies, Orthonormal bases of compactly supported wavelets, Communications on Pure and Applied Mathematics 41 (1998) 909– 996.
- R.M. Haralick, L.G. Shapiro, Computer and Robot Vision, vol. I, Addison-Wesley, Reading, MA, 1992.
- N. Jhanwar, S. Chaudhuri, G. Seetharaman, B. Zavidovique, Content based image retrieval using motif co-occurrence matrix, Image and Vision Computing 22 (2004) 1211–1220.
- H. Abrishami Moghaddam, T. Taghizadeh Khajoie, A.H. Rouhi, M. Saadatmand Tarzjan, Wavelet correlogram: a new approach for image indexing and retrieval, Pattern Recognition 38 (2005) 2506–2518.dbfbfn
- S. Liapis, G. Tziritas, Color and texture image retrieval using chromaticity histograms and wavelet frames, IEEE Transactions on Multimedia 6 (5) (2004) 676–686.
- B.C. Ko, H. Byun, FRIP: a region-based image retrieval tool using automatic image segmentation and stepwise Boolean and matching, IEEE Transactions on multimedia 7 (1) (2005) 105–113.
- Y.K. Chan, C.Y. Chen, Image retrieval system based on color-complexity and color-spatial features, The Journal of Systems and Software 71 (1–2) (2004) 65–70.
- C.C. Chang, Y.K. Chan, A fast filter for image retrieval based on color features,SEMS2000, Baden-Baden, German, 2000, pp. 47–51.
- H. Nezamabadi-Pour, E. Kabir, Image retrieval using histograms of uni-color and bi-color blocks and directional changes in intensity gradient, Pattern Recognition Letters 25 (14) (2004) 1547–1557.
- B.M. Mehtre, M. Kankanhalli, W.F. Lee, Shape measures for content-based image retrieval: comparison info, Processing and Management 33 (1997) 319–337.
- G.P. Babu, B.M. Mehtre, M.S. Kankanhalli, Color indexing for efficient image retrieval, Multimedia Tools and Applications 1 (4) (1995) 327–348.
Index Terms
Keywords