On preserving robustnessfalse alarm tradeoff in media hashing This paper discusses one of the important issues in generating a robust media hash. Robustness of a media hashing algorithm is primarily determined by three factors, (1) robustnessfalse alarm tradeoff achieved by the chosen feature representation, (2) accuracy of the bit extraction step and (3) the distance measure used to measure similarity (dissimilarity) between two hashes. The robustnessfalse alarm tradeoff in feature space is measured by a similarity (dissimilarity) measure and it defines a limit on the performance of the hashing algorithm. The distance measure used to compute the distance between the hashes determines how far this tradeoff in the feature space is preserved through the bit extraction step. Hence the bit extraction step is crucial, in defining the robustness of a hashing algorithm. Although this is recognized as an important requirement by all, to our knowledge there is no work in the existing literature that elucidates the efficacy of their algorithm based on their effectiveness in improving this tradeoff compared to other methods. This paper specifically demonstrates the kind of robustness false alarm tradeoff achieved by existing methods and proposes a method for hashing that clearly improves this tradeoff.

Fast Semantic Preserving Hashing for LargeScale CrossModal Retrieval Most Crossmodal hashing methods do not sufficiently exploit the discrimination power of semantic information when learning hash codes, while often involving timeconsuming training procedures for largescale dataset. To tackle these issues, we first formulate the learning of similaritypreserving hash codes in terms of orthogonally rotating the semantic data to hamming space, and then propose a novel Fast Semantic Preserving Hashing (FSePH) approach to largescale crossmodal retrieval. Specifically, FSePH introduces an orthonormal basis to regress the targeted hash codes of training examples to their corresponding reasonably relaxed class labels, featuring significantly reducing the quantization error. Meanwhile, an effective optimization algorithm is derived for modalityspecific projection function learning and an efficient closedform solution for hash code learning, which are computationally tractable. Extensive experiments have shown that the proposed FSePH approach runs sufficiently fast, and also significantly improves the retrieval performances over the stateofthearts.