Content Based Image Retrieval with Color Invariants

Abstract:

Content based image retrieval (CBIR) is an essential task in many applications. Color based methods have received much attention in past years, since color could serve efficiently for image retrieval, especially in the case of large database. However, there are two main drawbacks for color based image retrieval methods. Firstly, color based methods are not suitable for similar scenes under different illumination conditions, because color is sensitive to illumination. Secondly, existing approaches usually employ image descriptors with large size, which makes the approach unsuitable for real-time application. To overcome drawbacks mentioned above, an adaptive image retrieval method has been proposed, which integrates the color invariant with the spatial information about images. Different from previous methods, the quantization of the color space has not been manually determined. Instead, it has been decided according to the content of image, using an adaptive clustering technique. Therefore, the size of image descriptor is very small. In the proposed method, feature maps for images have been firstly established, which consist of color invariants. And then the Markov chain model has been employed to capture color information and spatial features. Finally, similar images are retrieved based on two-stage weighted distance. Experimental results show that the proposed method has improved simplicity and compactness of color based image retrieval methods, without the loss of efficiency and robustness.

• Periodical: Advanced Materials Research (Volumes 760-762)
• Pages: 1604-1608
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.760-762.1604
• Online since: September 2013
• Authors: Ya Hui Song, Xiao Chen, Shan Shan Qu
• Keywords: Color Invariant, Content Based Image Retrieval, Markov Chain Model

References

[1] J. Shashank, P. Kowshik, K. Srinathan, and C. V. Jawahar, Private Content Based Image Retrieval, presented at Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, (2008).

DOI: 10.1109/cvpr.2008.4587388

[2] R. Rahmani, S. A. Goldman, Z. Hui, S. R. Cholleti, and J. E. Fritts, Localized Content-Based Image Retrieval, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 30, pp.1902-1912, (2008).

DOI: 10.1109/tpami.2008.112

[3] C. Yixin, J. Z. Wang, and R. Krovetz, CLUE: cluster-based retrieval of images by unsupervised learning, Image Processing, IEEE Transactions on, vol. 14, pp.1187-1201, (2005).

DOI: 10.1109/tip.2005.849770

[4] Z. Ruofei and Z. Zhongfei, Hidden semantic concept discovery in region based image retrieval, presented at Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, (2004).

DOI: 10.1109/cvpr.2004.1315273

[5] K. ByoungChul and B. Hyeran, Integrated region-based image retrieval using region's spatial relationships, presented at Pattern Recognition, 2002. Proceedings. 16th International Conference on, (2002).

DOI: 10.1109/icpr.2002.1044649