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Image retrieval based on multiview constrained nonnegative matrix factorization and Gaussian mixture model spectral clustering method. (English) Zbl 1400.62139
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.

MSC:
62H35 Image analysis in multivariate analysis
Software:
CIFAR; Caltech-256
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[1] Wang, B.; Zhang, X.; Zhao, X.-Y.; Zhang, Z.-D.; Zhang, H.-X., A semantic description for content-based image retrieval, Proceedings of the 7th International Conference on Machine Learning and Cybernetics (ICMLC ’08)
[2] Thung, K.-H.; Paramesran, R.; Lim, C.-L., Content-based image quality metric using similarity measure of moment vectors, Pattern Recognition, 45, 6, 2193-2204, (2012) · Zbl 1234.68453
[3] Guo, J.-M.; Prasetyo, H., Content-based image retrieval using features extracted from halftoning-based block truncation coding, IEEE Transactions on Image Processing, 24, 3, 1010-1024, (2015)
[4] Kotoulas, L.; Andreadis, I., Colour histogram content-based image retrieval and hardware implementation, IEE Proceedings: Circuits, Devices and Systems, 150, 5, 387-393, (2003)
[5] Kokare, M.; Biswas, P. K.; Chatterji, B. N., Texture image retrieval using new rotated complex wavelet filters, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 35, 6, 1168-1178, (2005)
[6] Yu, J.; Rui, Y.; Chen, B., Exploiting click constraints and multi-view features for image re-ranking, IEEE Transactions on Multimedia, 16, 1, 159-168, (2014)
[7] Lyu, X.; Li, H.; Flierl, M., Hierarchically structured multi-view features for mobile visual search, Proceedings of the Data Compression Conference (DCC ’14)
[8] Yu, J.; Liu, D.; Tao, D.; Seah, H. S., On combining multiple features for cartoon character retrieval and clip synthesis, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42, 5, 1413-1427, (2012)
[9] Braun, M.; Hubay, K.; Magyari, E.; Veres, D.; Papp, I.; Bálint, M., Using linear discriminant analysis (LDA) of bulk lake sediment geochemical data to reconstruct lateglacial climate changes in the South Carpathian Mountains, Quaternary International, 293, 8, 114-122, (2013)
[10] Yeung, K. Y.; Ruzzo, W. L., Principal component analysis for clustering gene expression data, Bioinformatics, 17, 9, 763-774, (2001)
[11] Shi, X., Independent component analysis, IEEE Transactions on Neural Networks, 15, 735-741, (2001)
[12] Klema, V. C.; Laub, A. J., The singular value decomposition: its computation and some applications, IEEE Transactions on Automatic Control, 25, 2, 164-176, (1980) · Zbl 0433.93018
[13] Ewert, S.; Muller, M., Using score-informed constraints for NMF-based source separation, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ’12)
[14] Babji, S.; Tangirala, A. K., Source separation in systems with correlated sources using NMF, Digital Signal Processing, 20, 2, 417-432, (2010)
[15] Grais, E. M.; Erdogan, H., Regularized nonnegative matrix factorization using Gaussian mixture priors for supervised single channel source separation, Computer Speech and Language, 27, 3, 746-762, (2013)
[16] Lee, D. D.; Seung, H. S., Algorithms for non-negative matrix factorization, Advances in Neural Information Processing Systems, 13, 6, 556-562, (2001)
[17] Mao, J.-L., Geometric and Semantic similarity preserved non-negative matrix factorization for image retrieval, Proceedings of the International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP ’12)
[18] Babaee, M.; Bahmanyar, R.; Rigoll, G.; Datcu, M., Farness preserving nonnegative matrix factorization, Proceedings of the IEEE International Conference on Image Processing (ICIP ’14), IEEE
[19] Cai, D.; He, X.; Han, J.; Huang, T. S., Graph regularized nonnegative matrix factorization for data representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 8, 1548-1560, (2011)
[20] Liu, H.; Wu, Z.; Li, X.; Cai, D.; Huang, T. S., Constrained nonnegative matrix factorization for image representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 7, 1299-1311, (2012)
[21] Xiao, Y.; Zhu, Z.; Zhao, Y.; Wei, Y.; Wei, S.; Li, X., Topographic NMF for data representation, IEEE Transactions on Cybernetics, 44, 10, 1762-1771, (2014)
[22] Khan, Y. D.; Ahmad, F.; Khan, S. A., Content-based image retrieval using extroverted semantics: a probabilistic approach, Neural Computing and Applications, 24, 7-8, 1735-1748, (2014)
[23] Padala, A.; Yarramalle, S.; Mhm, K. P., An approach for effective image retrievals based on semantic tagging and generalized gaussian mixture model, International Journal of Information Engineering and Electronic Business, 7, 3, 39-44, (2015)
[24] Zeng, S.; Huang, R.; Wangc, H.; Kang, Z., Image retrieval using spatiograms of colors quantized by gaussian mixture models, Neurocomputing, 171, 1, 673-684, (2015)
[25] Piatek, M. L.; Smolka, B., Color image retrieval based on spatiochromatic multichannel gaussian mixture modelling, Proceedings of the 8th International Symposium on Image and Signal Processing and Analysis (ISPA ’13)
[26] Marakakis, A.; Galatsanos, N.; Likas, A.; Stafylopatis, A., Stafylopatis, application of relevance feedback in content based image retrieval using Gaussian mixture models, Proceedings of the IEEE International Conference on Tools with Artificial Intelligence
[27] He, X.; Cai, D.; Shao, Y.; Bao, H.; Han, J., Laplacian regularized Gaussian mixture model for data clustering, IEEE Transactions on Knowledge and Data Engineering, 23, 9, 1406-1418, (2011)
[28] Roy, A.; Parui, S. K.; Roy, U., SWGMM: a semi-wrapped Gaussian mixture model for clustering of circular-linear data, Pattern Analysis and Applications, 19, 3, 631-645, (2016)
[29] Zeng, S.; Huang, R.; Kang, Z.; Sang, N., Image segmentation using spectral clustering of Gaussian mixture models, Neurocomputing, 144, 346-356, (2014)
[30] Lee, D. D.; Seung, H. S., Learning the parts of objects by non-negative matrix factorization, Nature, 401, 6755, 788-791, (1999) · Zbl 1369.68285
[31] Yan, X.; Xiong, W.; Hu, L.; Wang, F.; Zhao, K., Missing value imputation based on gaussian mixture model for the internet of things, Mathematical Problems in Engineering, 2015, (2015)
[32] Oliva, A.; Torralba, A., Modeling the shape of the scene: a holistic representation of the spatial envelope, International Journal of Computer Vision, 42, 3, 145-175, (2001) · Zbl 0990.68601
[33] Triggs, N. D. B., Histograms of oriented gradients for human detection, CVPR Journals, 1, 12, 886-893, (2005)
[34] Ahonen, T.; Hadid, A.; Pietikinen, M., Face recognition with local binary patterns, Proceedings of the 8th European Conference on Computer Vision
[35] Han, J.; Ma, K.-K., Fuzzy color histogram and its use in color image retrieval, IEEE Transactions on Image Processing, 11, 8, 944-952, (2002)
[36] Li, J.-S.; Pan, Y.-L., A note on the second largest eigenvalue of the Laplacian matrix of a graph, Linear and Multilinear Algebra, 48, 2, 117-121, (2000) · Zbl 0979.15016
[37] Boyd; Vandenberghe; Faybusovich, Convex optimization, IEEE Transactions on Automatic Control, 51, 11, 1859, (2006)
[38] Goldberger, S. G. J.; Greenspan, H., An efficient image similarity measure based on approximations of kl-divergence between two Gaussian mixtures, Proceedings of the IEEE International Conference on Computer Vision (ICCV ’03)
[39] Banerjee, A.; Jost, J., On the spectrum of the normalized graph Laplacian, Linear Algebra and Its Applications, 428, 11-12, 3015-3022, (2008) · Zbl 1149.05327
[40] Griffin, G.; Holub, A.; Perona, P., Caltech-256 Object Category Dataset, (2007), California Institute of Technology
[41] Krizhevsky, A.; Hinton, G., Learning multiple layers of features from tiny images, TR-2009, (2009), Toronto, Canada: Department of Computer Science, University of Toronto, Toronto, Canada
[42] Charles, Y. R.; Ramraj, R., A novel local mesh color texture pattern for image retrieval system, AEU—International Journal of Electronics and Communications, 70, 3, 225-233, (2016)
[43] Liu, L.; Yu, M.; Shao, L., Multiview alignment hashing for efficient image search, IEEE Transactions on Image Processing, 24, 3, 956-966, (2015)
[44] Cui, S.; Datcu, M., Comparison of kullback-leibler divergence approximation methods between gaussian mixture models for satellite image retrieval, Proceedings of the IEEE Geoscience and Remote Sensing Symposium (IGARSS ’15)
[45] Liu, J.; Wang, C.; Gao, J.; Han, J., Multi-view clustering via joint nonnegative matrix factorization, Proceedings of the 2013 SIAM International Conference on Data Mining
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