Image retrieval using noisy query

In conventional content based image retrieval (CBIR) employing relevance feedback, one implicit assumption is that both pure positive and negative examples are available. However it is not always true in the practical applications of CBIR. In this paper, we address a new problem of image retrieval using several unclean positive examples, named noisy query, in which some mislabeled images or weak relevant images present. The proposed image retrieval scheme measures the image similarity by combining multiple feature distances. Incorporating data cleaning and noise tolerant classifier, a twostep strategy is proposed to handle noisy positive examples. Experiments carried out on a subset of Corel image collection show that the proposed scheme outperforms the competing image retrieval schemes.