IEEE Trans Image Process - A sparse embedding and least variance encoding approach to hashing.

Tópicos

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Resumo

Hashing is becoming increasingly important in large-scale image retrieval for fast approximate similarity search and efficient data storage. Many popular hashing methods aim to preserve the kNN graph of high dimensional data points in the low dimensional manifold space, which is, however, difficult to achieve when the number of samples is big. In this paper, we propose an effective and efficient hashing approach by sparsely embedding a sample in the training sample space and encoding the sparse embedding vector over a learned dictionary. To this end, we partition the sample space into clusters via a linear spectral clustering method, and then represent each sample as a sparse vector of normalized probabilities that it falls into its several closest clusters. This actually embeds each sample sparsely in the sample space. The sparse embedding vector is employed as the feature of each sample for hashing. We then propose a least variance encoding model, which learns a dictionary to encode the sparse embedding feature, and consequently binarize the coding coefficients as the hash codes. The dictionary and the binarization threshold are jointly optimized in our model. Experimental results on benchmark data sets demonstrated the effectiveness of the proposed approach in comparison with state-of-the-art methods.

Resumo Limpo

hash becom increas import largescal imag retriev fast approxim similar search effici data storag mani popular hash method aim preserv knn graph high dimension data point low dimension manifold space howev difficult achiev number sampl big paper propos effect effici hash approach spars embed sampl train sampl space encod spars embed vector learn dictionari end partit sampl space cluster via linear spectral cluster method repres sampl spars vector normal probabl fall sever closest cluster actual emb sampl spars sampl space spars embed vector employ featur sampl hash propos least varianc encod model learn dictionari encod spars embed featur consequ binar code coeffici hash code dictionari binar threshold joint optim model experiment result benchmark data set demonstr effect propos approach comparison stateoftheart method

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