A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization

Automatic image annotation is for more accurate image retrieval and classification by assigning labels to images. This paper proposes a semisupervised framework based on graph embedding and multiview nonnegative matrix factorization (GENMF) for automatic image annotation with multilabel images. Firs...

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Main Authors:	Hongwei Ge, Zehang Yan, Jing Dou, Zhen Wang, ZhiQiang Wang
Format:	Article
Language:	English
Published:	Hindawi Limited 2018-01-01
Series:	Mathematical Problems in Engineering
Online Access:	http://dx.doi.org/10.1155/2018/5987906

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spelling	doaj-cc1c5bf731ae4a278675654996f73c1f2020-11-24T21:41:30ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/59879065987906A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix FactorizationHongwei Ge0Zehang Yan1Jing Dou2Zhen Wang3ZhiQiang Wang4School of Computer Science and Technology, Dalian University of Technology, Dalian 116023, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116023, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116023, ChinaSchool of Mathematical Science, Dalian University of Technology, Dalian 116023, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Dalian 116023, ChinaAutomatic image annotation is for more accurate image retrieval and classification by assigning labels to images. This paper proposes a semisupervised framework based on graph embedding and multiview nonnegative matrix factorization (GENMF) for automatic image annotation with multilabel images. First, we construct a graph embedding term in the multiview NMF based on the association diagrams between labels for semantic constraints. Then, the multiview features are fused and dimensions are reduced based on multiview NMF algorithm. Finally, image annotation is achieved by using the new features through a KNN-based approach. Experiments validate that the proposed algorithm has achieved competitive performance in terms of accuracy and efficiency.http://dx.doi.org/10.1155/2018/5987906
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Hongwei Ge Zehang Yan Jing Dou Zhen Wang ZhiQiang Wang
spellingShingle	Hongwei Ge Zehang Yan Jing Dou Zhen Wang ZhiQiang Wang A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization Mathematical Problems in Engineering
author_facet	Hongwei Ge Zehang Yan Jing Dou Zhen Wang ZhiQiang Wang
author_sort	Hongwei Ge
title	A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization
title_short	A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization
title_full	A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization
title_fullStr	A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization
title_full_unstemmed	A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization
title_sort	semisupervised framework for automatic image annotation based on graph embedding and multiview nonnegative matrix factorization
publisher	Hindawi Limited
series	Mathematical Problems in Engineering
issn	1024-123X 1563-5147
publishDate	2018-01-01
description	Automatic image annotation is for more accurate image retrieval and classification by assigning labels to images. This paper proposes a semisupervised framework based on graph embedding and multiview nonnegative matrix factorization (GENMF) for automatic image annotation with multilabel images. First, we construct a graph embedding term in the multiview NMF based on the association diagrams between labels for semantic constraints. Then, the multiview features are fused and dimensions are reduced based on multiview NMF algorithm. Finally, image annotation is achieved by using the new features through a KNN-based approach. Experiments validate that the proposed algorithm has achieved competitive performance in terms of accuracy and efficiency.
url	http://dx.doi.org/10.1155/2018/5987906
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A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization

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