Contact Project Developer Ashish D. Tiwari [astiwz@gmail.com]
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MULTIVIEW ALIGNMENT HASHING FOR EFFICIENT IMAGE SEARCH

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 Ha
Abstract-Synopsis-Documentation

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.

EXISTING SYSTEM:

However, single-view hashing is the main topic on which the previous exploration of hashing methods focuses. In their architectures, only one type of feature descriptor is used for learning hashing functions. In practice, to make a more comprehensive description, objects/images are always represented via several different kinds of features and each of them has its own characteristics. Thus, it is desirable to incorporate these heterogenous feature descriptors into learning hashing functions, leading to multi-view hashing approaches. Multiview learning techniques have been well explored in the past few years and widely applied to visual information fusion. Recently, a number of multiview hashing methods have been proposed for efficient similarity search, such as Multi-View Anchor Graph Hashing (MVAGH), Sequential Update for Multi- View Spectral Hashing (SU-MVSH) , Multi-View Hashing (MVH-CS), Composite Hashing with Multiple Information Sources (CHMIS) and Deep Multi-view Hashing (DMVH). These methods mainly depend on spectral, graph or deep learning techniques to achieve data structure preserving encoding. Nevertheless, the hashing purely with the above schemes are usually sensitive to data noise and suffering from the high computational complexity. The above drawbacks of prior work motivate us to propose a novel unsupervised mulitiview hashing approach, termed Multiview Alignment Hashing (MAH), which can effectively fuse multiple information sources and exploit the discriminative low-dimensional embedding via Nonnegative Matrix Factorization (NMF). NMF is a popular method in data mining tasks including clustering, collaborative filtering, outlier detection, etc. Unlike other embedding methods with positive and negative values, NMF seeks to learn a nonnegative parts-based representation that gives better visual interpretation of factoring matrices for high-dimensional data. Therefore, in many cases, NMF may be more suitable for subspace learning tasks, because it provides a non-global basis set which intuitively contains the localized parts of objects. In addition, since the flexibility of matrix factorization can handle widely varying data distributions, NMF enables more robust subspace learning. More importantly, NMF decomposes an original matrix into a part-based representation that gives better interpretation of factoring matrices for non-negative data. When applying NMF to multiview fusion tasks, a part-based representation can reduce the corruption between any two views and gain more discriminative codes.

PROPOSED SYSTEM:

To the best of our knowledge, this is the first work using NMF to combine multiple views for image hashing. It is worthwhile to highlight several contributions of the proposed method: • MAH can find a compact representation uncovering the hidden semantics from different view aspects and simultaneously respecting the joint probability distribution of data. • To solve our nonconvex objective function, a new alternate optimization has been proposed to get the final solution. • We utilize multivariable logistic regression to generate the hashing function and achieve the out-of-sample extension. In this paper, we present a Regularized Kernel Nonnegative Matrix Factorization (RKNMF) framework for hashing, which can effectively preserve the data intrinsic probability distribution and simultaneously reduce the redundancy of low-dimensional representations. Rather than locality-based graph regularization, we measure the joint probability of pairwise data by the Gaussian function, which is defined over all the potential neighbors and has been proved to effectively resist data noise . This kind of measurement is capable to capture the local structure of the high-dimensional data while also revealing global structure such as the presence of clusters at several scales. To the best of our knowledge, this is the first time that NMF with multiview hashing has been successfully applied to feature embedding for large-scale similarity search.

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