Affiliation:
1. Dalian Maritime University
Abstract
The basic idea of this paper is multiple keywords can be assigned to image through the method of fixed region segmentation. We divide a single image into the 4-level regions. For each of them, the combined feature is extracted and inputted into the trained Fuzzy SVMs to classify, which has been proved better than conventional SVMs in the generalization ability. The values of classification in each category are calculated. Based on these values, the keywords are assigned.
Publisher
Trans Tech Publications, Ltd.
Reference13 articles.
1. H.L. WAN, & U.C. Morshed, Image Semantic Classification by Using SVM, Journal of Software, Vol 14, No. 11, pp.1891-1899, (2003).
2. Hong, P., Tian, Q. and Huang, T. S. Incorporate Support Vector Machines to content-based image retrieval with relevance feedback, Proceedings of the IEEE International Conference on Image Processing, Vancouver, Canada, pp.750-753, September (2000).
3. Xiang Sean Zhou, Thomas S. Huang. Relevance feedback in image retrieval: A comprehensive review, Multimedia Systems, Vol 8, No. 6, pp.536-544, (2003).
4. Simon Tong, Edward Chang. Support vector machine active learning for image retrieval, Proceedings of the ninth ACM international conference on Multimedia, Ottawa, Canada, p.107 – 118, September, (2001).
5. Chapelle O, Haffner P, Vapnik V. SVMs for histogram-based image classification,. IEEE Transactions on Neural Network, special issue on Support Vectors, Vol 10, No. 5, pp.1055-1065, (1999).
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献