The role of sample distribution in relevance feedback for content based image retrieval

Most current relevance feedback algorithms rely only on labeled samples, and ignore the possible role of the distribution of all samples. We investigate the potential role of sample distribution in relevance feedback. We present a probabilistic framework for relevance feedback in content-based image retrieval. Based on Bayes rule, this framework combines the probability densities of relevant samples and those of all samples in a function to rank images. In this way, the characteristic of the distribution of all samples is taken into account to improve the performance. The density estimation is conducted with non-parametric density estimation, and the densities of all samples can be predetermined before any query. The new approach was evaluated on a database of 20,000 images and compared to some current solutions. Experimental results have demonstrated the effectiveness of our approach.