A novel framework for SVM-based image retrieval on large databases
In this paper, a novel framework is proposed to deliver a fast, robust, and generally applicable SVM-based image re- trieval for large databases. A quick test scheme is developed, and on-line kernel learning is employed to realize it after analyzing the relationship between them. Then an upper bound on maximum test scope is derived to speed up test in further. Also, the general applicability is well maintained because this framework does not need a kernel function and index structure to be pre-defined. Taking the advantages of this framework, more sophisticated SVM can be used to improve retrieval performance while keeping short response time. Experimental results on large image databases verify the effectiveness and efficiency of the proposed framework.