Using Relevance Feedback to Bridge the Semantic Gap
In this article relevant developments in relevance feedback based image annotation and retrieval are reported. A new approach to infer semantic concepts representing meaningful objects in images is also described. The proposed technique combines user relevance feedback and underlying low-level properties of elementary building blocks making up semantic objects in images. Images are regarded as mosaics made of small building blocks featuring good representations of colour, texture and edgeness. The approach is based on accurate classification of these building blocks. Once this has been achieved, a signature for the object of concern is built. It is expected that this signature features a high discrimination power and consequently it becomes very suitable to find other images containing the same semantic object. The model combines fuzzy clustering and relevance feedback in the training stage, and uses fuzzy support vector machines in the generalization stage.
KeywordsFeature Space Image Retrieval Relevance Feedback Semantic Concept Content Base Image Retrieval
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- 1.O’Reilly, J.: Content Engineering. Electronics Communications Engineering Journal 14(4) (August 2002)Google Scholar
- 3.Vailaya, A., Jain, A., Zhang, H.-J.: On image classification: city vs. landscape. In: Proc. IEEE Workshop on Content-Based Access of Image and Video Libraries, pp. 3–8 (1998)Google Scholar
- 5.Cox, J., Miller, M., Minka, T., Yianilos, P.: An Optimized Interaction Strategy for Bayesian Relevance Feedback. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 553–558 (1998)Google Scholar
- 7.Jing, F., Li, M., Zhang, H.-J., Zhang, B.: Relevance Feedback in Region-Based Image Retrieval. IEEE Transactions on Circuits and Systems for Video Technology 14(5) (2004)Google Scholar
- 8.Rui, Y., Huan, T.S., Mehrotra., S.: Content-based Image Retrieval with Relevance Feedback in MARS. In: Proceedings of IEEE Int. Conf. on Image Processing, pp. 26–29 (1997)Google Scholar
- 10.Wu, K., Yap, K.H.: Fuzzy relevance feedback in content-based image retrieval. In: Proc. Int. Conf. Information and Signal Processing and Pacific-Rim Conf. Multimedia, Singpore (2003)Google Scholar
- 11.Tian, Q., Wu, Y., Huang, T.S.: Incorporate Discriminate Analysis with EM Algorithm in Image Retrieval. In: Proc. IEEE International Conf. on Multimedia and Expo. (2000)Google Scholar
- 12.Wu, Y., Tian, Q., Huang, T.S.: Integrating Unlabeled Images for Image Retrieval Based on Relevance Feedback. In: Proc. of the 15th Int’l Conf. on Pattern Recognition, vol. 1, pp. 21–24 (2000)Google Scholar
- 17.Wu, X., Srihari, R.: Incorporating prior knowledge with weighted margin support vector machines. In: Proceedings of the international conference on Knowledge discovery and data mining, pp. 326–333 (2004) ISBN:1-58113-888-9Google Scholar
- 18.Dorado, A., Djordjevic, D., Pedrycz, W., Izquierdo, E.: Efficient image selection for concept learning. In: IEE Proceedings Vision, Image & Signal Processing (to appear, 2005)Google Scholar