Visual re-ranking has received considerable attention in recent years. It aims to enhance the performance of text-based image retrieval by boosting the rank of relevant images using visual information. Hypergraph has been widely used for relevance estimation, where textual results are taken as vertices and the re-ranking problem is formulated as a transductive learning on the hypergraph. The potential of the hypergraph learning is essentially determined by the hypergraph construction scheme. To this end, in this paper, we introduce a novel data representation technique named adaptive collaborative representation for hypergraph learning. Compared to the conventional collaborative representation, we consider the data locality to adaptively select relevant and close samples for a test sample and discard irrelevant and faraway ones. Moreover, at the feature level, we impose a weight matrix on the representation errors to adaptively highlight the important features and reduce the effect of redundant/noisy ones. Finally, we also add a nonnegativity constraint on the representation coefficients to enhance the hypergraph interpretability. These attractive properties allow constructing a more informative and quality hypergraph, thereby achieving better retrieval performance than other hypergraph models. Extensive experiments on the public MediaEval benchmarks demonstrate that our re-ranking method achieves consistently superior results, compared to state-of-the-art methods.
Social and Mobile are the two very characterizing trends of the Internet. Subsequently, the volume of photos with rich social, textual and contextual information increases exponentially either on mobile devices or social networks. Performing an efficient and effective mobile Image Search over social photo collection is therefore a crucial challenge. Indeed, capture the complex connections among social photos is as important as speeding up similarity search at large scale. This paper present a generic Mobile Image Search framework with hypergraph hashing. On the mobile side, users are enabled to formulate whether visual, textual or vocal queries. On the server side, we start by modeling complex connections that may exist among photos and social features using an hypergraph. To accelerate the nearest neighbor search over the hypergraph, a spectral hashing is performed. Namely, each hypergraph vertex is mapped to a binary string without loss of similarity. For unseen items in the hypergraph, a query-adaptive supervised learning is carried out to learn binary strings based on the query type. We report the initial results over NUS-WIDE collection which show that the proposed framework is promising in the field of Mobile Image Search.
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