Weight revision and SVM-based relevance feedback algorithm for content-based image retrieval

To improve the efficiency of image relevance feedback algorithm rapidly, an algorithm of auto-adapted weight revision combining with support vector machine is proposed. In early retrieval stage, the weight coefficients of different features are adjusted quickly by auto-adapted weight revision algorithm, using quick deletion strategy of negative samples to improve the accuracy of early retrieval stage, which providing more positive samples for the SVM models in later retrieval stage; In later retrieval period, retrieval models are designed by SVM models, and they are optimized by the algorithm of active learning and semi-supervision relevance feedback. Experiment results on 5000 Corel images database indicate that this algorithm can obviously improve the efficiency and performance of learning machine and accelerate the convergence to user´s inquiry concept.