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Monday, 26 February 2018

Multiview Alignment Hashing for Efficient Image Search

Multiview Alignment Hashing for

 Efficient Image Search

Abstract— Hashing is a popular and efficient method for nearest neighbor search in large-scale data spaces by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension. For most hashing methods, the performance of retrieval heavily depends on the choice of the high-dimensional feature descriptor. Furthermore, a single type of feature cannot be descriptive enough for different images when it is used for hashing. Thus, how to combine multiple representations for learning effective hashing functions is an imminent task. In this paper, we present a novel unsupervised multiview alignment hashing approach based on regularized kernel nonnegative matrix factorization, which can find a compact representation uncovering the hidden semantics and simultaneously respecting the joint probability distribution of data. In particular, we aim to seek a matrix factorization to effectively fuse the multiple information sources meanwhile discarding the feature redundancy. Since the raised problem is regarded as nonconvex and discrete, our objective function is then optimized via an alternate way with relaxation and converges to a locally optimal solution. After finding the low-dimensional representation, the hashing functions are finally obtained through multivariable logistic regression. The proposed method is systematically evaluated on three data sets: 1) Caltech-256; 2) CIFAR-10; and 3) CIFAR-20, and the results show that our method significantly outperforms the state-of-the-art multiview hashing techniques.Multiview Alignment Hashing for Efficient Image Search
CONCLUSION In this paper, we have presented a novel unsupervised hashing method called Multiview Alignment Hashing (MAH), where hashing functions are effectively learnt via kernelized Nonnegative Matrix Factorization with preserving data joint probability distribution. We incorporate multiple visual features from different views together and an alternate way is introduced to optimize the weights for different views and simultaneously produce the low-dimensional representation. We address this as a nonconvex optimization problem and its alternate procedure will finally converge at the locally optimal solution. For the out-of-sample extension, multivariable logistic regression has been successfully applied to obtain the regression matrix for fast hash encoding. Numerical experiments have been systematically evaluated on Caltech-256, CIFAR-10 and CIFAR-20 datasets. The results manifest that our MAH significantly outperforms the state-of-the-art multiview hashing techniques in terms of searching accuracies.

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