Image clustering by hyper-graph regularized non-negative matrix factorization

Image clustering is a critical step for the applications of content-based image retrieval, image annotation and other high-level image processing. To achieve these tasks, it is essential to obtain proper representation of the images. Non-negative Matrix Factorization (NMF) learns a part-based repres...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 138; S. 209 - 217
Hauptverfasser:	Zeng, Kun, Yu, Jun, Li, Cuihua, You, Jane, Jin, Taisong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 22.08.2014 Elsevier
Schlagworte:	Algorithms Applied sciences Artificial intelligence Clustering Computer science; control theory; systems Computer systems and distributed systems. User interface Data processing. List processing. Character string processing Dimension reduction Exact sciences and technology Factorization Graphs Hyper-graph laplacian Image clustering Image processing Information retrieval. Graph Manifold regularization Manifolds Memory organisation. Data processing Non-negative matrix factorization Pattern recognition. Digital image processing. Computational geometry Representations Software Theoretical computing Hyper-graph laplacian Dimension reduction Image clustering Non-negative matrix factorization Manifold regularization Cluster analysis Laplace operator Brain Image recognition Tree(graph) Image processing Central nervous system Matrix factorization Encephalon Geometrical structure Multidimensional analysis Graph connectivity Semi-supervised learning Computer vision Image retrieval Graph theory Pattern recognition Ordered graph Laplacian Image analysis Non negative matrix Content-based retrieval Geometrical method Reduced order model Regularization Indexing
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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