An Effective Similarity Measurement Algorithm for Dominant Color Feature Matching in Image Retrieval

Abstract:

For the reason that dominant colors can characterize color information of image region and can represent the image using fewer dimensions, it is one of the widely used color features in image retrieval. We extract the dominant color feature in HSV color space, and combine it with color distribution information. In this paper, a new similarity measurement algorithm based on block distance is proposed for dominant color matching. Our proposed algorithm not only takes the distance between dominant colors into account, but also the difference of the percentage of dominant colors. The average precision of our algorithm improves about 5% and about 14% respectively compared with block distance and Euclidean distance. Although the average precision of our algorithm is almost equal to quadratic form distance, the computation cost of our algorithm is obviously less than it.

• Periodical: Applied Mechanics and Materials (Volumes 182-183)
• Pages: 1169-1173
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.182-183.1169
• Online since: June 2012
• Authors: Li Fang Yang, Xiang Lin Huang, Rui Lv, Hui Lv
• Keywords: Color Space, Dominant Color, Image Retrieval, Similarity Measurement

References

[1] Samia G. Omar, Mohamed A. Ismail and Sahar M. Ghanem. WAY-LOOK4: A CBIR System Based on Class Signature of the Images' Color and Texture Features. AICCSA 2009, pp: 464-471.

DOI: 10.1109/aiccsa.2009.5069365

[2] Lu Hui, Huang Xianglin, Yang Lifang, Liu Min. A Relevance Feedback Systems for CBIR with Long-term Learning. International conference on multimedia information networking and security 2010, pp: 700-704.

DOI: 10.1109/mines.2010.151

[3] B. S. Manjunath, Jens-Rainer Ohm, Vinod V. Vasudevan and Akio Yamada. Color and Texture Descriptors. IEEE transactions on circuits and systems for video technology, Vol. 11, No. 6, 2001, pp: 703-715.

DOI: 10.1109/76.927424

[4] Ka-Man Wong, Lai-Man Po and Kwok-Wai Cheung. A Compact and Efficient Color Descriptor for Image Retrieval. ICME 2007, pp: 611-614.

DOI: 10.1109/iscas.2005.1464894

[5] Dr. N. Krishnan, M. Sheerin Banu, C. Callins Christiyana. Content Based Image Retrieval Using Dominant Color Identification Based on Foreground Objects. International conference on computational intelligence and multimedia applications 2007, pp: 190-194.

DOI: 10.1109/iccima.2007.449

[6] Ka-Man Wong, Chun-Ho Cheung, Tak-Shing Liu and Lai-Man Po. Dominant Color Image Retrieval using Merged Histogram. IEEE 2003, pp: II908-911.

DOI: 10.1109/iscas.2003.1206122

[7] Hong Shao, Yueshu Wu, Wencheng Cui, Jinxia Zhang. Image Retrieval Based on MPEG-7 Dominant Color Descriptor. The 9th International Conference for Young Computer Scientists, 2008, pp: 753-757.

DOI: 10.1109/icycs.2008.89

[8] Quan Zhang, Xiaoying Tai. Endoscope Image Retrieval Based on Color Feature Fusion. Congress on Image and Signal Processing 2008, pp: 247-251.

DOI: 10.1109/cisp.2008.247

[9] Han Ling, Zhang Zuogang, Sun Jingguang, Zhang Xinjun. A Color-Based Image Retrieval Approach. Computer & Digital Engineering, Vol. 36, No. 2, 2008, pp: 118-121.

[10] Lin Kezheng, Zhang Caihua, Liupie. Image Retrieval Based on Sub-block Dominant Color Mating. Computer Engineering, 2010, Vol. 36, No. 13, pp: 186-188.

[11] Yining Deng, B. S. Manjunath, Charles Kenney, Michael S. Moore, and Hyundoo Shin. An Efficient Color Representation for Image Retrieval. IEEE transactions on image processing, Vol. 10, No. 1, 2001, pp: 140-147.

DOI: 10.1109/83.892450

[12] Xu Huiying, Yuan Jie, Zhao Jianmin, Zhu Xinzhong. Image Retrieval Based on Color and Texture. Computer Science, 2009, Vol. 36, No. 5, pp: 282-286.