Click-boosted graph ranking for image retrieval

Abstract

Graph ranking is one popular and successful technique for image retrieval, but its effectiveness is often limited by the well-known semantic gap. To bridge this gap, one of the current trends is to leverage the click-through data associated with images to facilitate the graph-based image ranking. However, the sparse and noisy properties of the image click-through data make the exploration of such resource challenging. Towards this end, this paper propose a novel click-boosted graph ranking framework for image retrieval, which consists of two coupled components. Concretely, the first one is a click predictor based on matrix factorization with visual regularization, in order to alleviate the sparseness of the click-through data. The second component is a soft-label graph ranker that conducts the image ranking by using the enriched click-through data noise-tolerantly. Extensive experiments for the tasks of click predicting and image ranking validate the effectiveness of the proposed methods in comparison to several existing approaches.

On approximate k-nearest neighbor searches based on the earth mover’s distance for efficient content-based multimedia information retrieval

“…Due to the recent advances in digital technologies, an enormous number of multimedia objects are now available over the Internet. In order to find the specific objects needed by the users from such a huge multimedia pool, an efficient search technique is highly essential [1], [2], [3], [4], [28]. Content-based information retrieval (CBIR) is the search technique based on the contents of multimedia objects.…”