Given a pair of images each described by a feature set, image similarity is defined by comparing the feature set on the basis of a similarity function. In a typical Visual Information Retrieval system, while searching for a query image among the elements of the data set of images, knowledge of the domain will be expressed by formulating a similarity measure between the query and data set based on some visual features. Therefore, measuring meaningful image similarity consists of two intrinsic elements: finding a set of features for adequately describing the image content and finding a suitable metric for assessing the similarity on the basis of feature space. The feature set can be computed globally for the entire image or locally for a small group of pixels such as regions or objects. The similarity measure can be different depending on the types of features. Typically, the feature space is assumed to be...
KeywordsManifold Covariance Coherence Pyramid
- 2.Datta R, Joshi D, Li J, Wang JZ. Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv. 2008;40(65)Google Scholar
- 3.Fei-Fei L., Perona P. A bayesian hierarchical model for learning natural scene categories. In Proceedings of IEEE International Conference on Computer Vision and Pattersn Recognition; 2005. p. 524–31.Google Scholar
- 5.Long F, Zhang H-J, Feng DD. Fundamentals of content-based image retrieval. In: Feng DD, Siu WC, Zhang H-J, editors. Multimedia information retrieval and management – technological fundamentals and applications. Berlin/Heidelberg/New York: Springer; 2003.Google Scholar
- 12.Wang B., Li Z., Li M., Ma W.-Y. Large-scale duplicate detection for web image search. In Proceedings of IEEE International Conference on Multimedia and Expo; 2006. P. 353–6.Google Scholar
- 13.Zhou D., Bousquet O., Lal T., Weston J., Scholkopf B. Learning with local and global consistency. In Proceedings of Advances in Neural Information Processing System; 2003. p. 321–8.Google Scholar