Image retrieval based on multiview constrained nonnegative matrix factorization and Gaussian mixture model spectral clustering method
type: Item
instance of: scholarly article
title: Image retrieval based on multiview constrained nonnegative matrix factorization and Gaussian mixture model spectral clustering method
zbMATH Open document ID: 1400.62139
DOI: 10.1155/2016/3271924
author: Qunyi Xie
author: Hongqing Zhu
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: Content-based image retrieval has recently become an important research topic and has been widely used for managing images from repertories. In this article, we address an efficient technique, called MNGS, which integrates multiview constrained nonnegative matrix factorization (NMF) and Gaussian mixture model- (GMM-) based spectral clustering for image retrieval. In the proposed methodology, the multiview NMF scheme provides competitive sparse representations of underlying images through decomposition of a similarity-preserving matrix that is formed by fusing multiple features from different visual aspects. In particular, the proposed method merges manifold constraints into the standard NMF objective function to impose an orthogonality constraint on the basis matrix and satisfy the structure preservation requirement of the coefficient matrix. To manipulate the clustering method on sparse representations, this paper has developed a GMM-based spectral clustering method in which the Gaussian components are regrouped in spectral space, which significantly improves the retrieval effectiveness. In this way, image retrieval of the whole database translates to a nearest-neighbour search in the cluster containing the query image. Simultaneously, this study investigates the proof of convergence of the objective function and the analysis of the computational complexity. Experimental results on three standard image datasets reveal the advantages that can be achieved with the proposed retrieval scheme.
Mathematics Subject Classification ID: 62H35
zbMATH DE Number: 6953041
describes a project that uses: CIFAR
describes a project that uses: Caltech-256
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/3271924
OpenAlex ID: W2561246941
Wikidata QID: Q59130844
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description: scientific article; zbMATH DE number 6953041