Cargando…
Multilabel Image Annotation Based on Double-Layer PLSA Model
Due to the semantic gap between visual features and semantic concepts, automatic image annotation has become a difficult issue in computer vision recently. We propose a new image multilabel annotation method based on double-layer probabilistic latent semantic analysis (PLSA) in this paper. The new d...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4066723/ https://www.ncbi.nlm.nih.gov/pubmed/24999490 http://dx.doi.org/10.1155/2014/494387 |
_version_ | 1782322204769255424 |
---|---|
author | Zhang, Jing Li, Da Hu, Weiwei Chen, Zhihua Yuan, Yubo |
author_facet | Zhang, Jing Li, Da Hu, Weiwei Chen, Zhihua Yuan, Yubo |
author_sort | Zhang, Jing |
collection | PubMed |
description | Due to the semantic gap between visual features and semantic concepts, automatic image annotation has become a difficult issue in computer vision recently. We propose a new image multilabel annotation method based on double-layer probabilistic latent semantic analysis (PLSA) in this paper. The new double-layer PLSA model is constructed to bridge the low-level visual features and high-level semantic concepts of images for effective image understanding. The low-level features of images are represented as visual words by Bag-of-Words model; latent semantic topics are obtained by the first layer PLSA from two aspects of visual and texture, respectively. Furthermore, we adopt the second layer PLSA to fuse the visual and texture latent semantic topics and achieve a top-layer latent semantic topic. By the double-layer PLSA, the relationships between visual features and semantic concepts of images are established, and we can predict the labels of new images by their low-level features. Experimental results demonstrate that our automatic image annotation model based on double-layer PLSA can achieve promising performance for labeling and outperform previous methods on standard Corel dataset. |
format | Online Article Text |
id | pubmed-4066723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-40667232014-07-06 Multilabel Image Annotation Based on Double-Layer PLSA Model Zhang, Jing Li, Da Hu, Weiwei Chen, Zhihua Yuan, Yubo ScientificWorldJournal Research Article Due to the semantic gap between visual features and semantic concepts, automatic image annotation has become a difficult issue in computer vision recently. We propose a new image multilabel annotation method based on double-layer probabilistic latent semantic analysis (PLSA) in this paper. The new double-layer PLSA model is constructed to bridge the low-level visual features and high-level semantic concepts of images for effective image understanding. The low-level features of images are represented as visual words by Bag-of-Words model; latent semantic topics are obtained by the first layer PLSA from two aspects of visual and texture, respectively. Furthermore, we adopt the second layer PLSA to fuse the visual and texture latent semantic topics and achieve a top-layer latent semantic topic. By the double-layer PLSA, the relationships between visual features and semantic concepts of images are established, and we can predict the labels of new images by their low-level features. Experimental results demonstrate that our automatic image annotation model based on double-layer PLSA can achieve promising performance for labeling and outperform previous methods on standard Corel dataset. Hindawi Publishing Corporation 2014 2014-06-04 /pmc/articles/PMC4066723/ /pubmed/24999490 http://dx.doi.org/10.1155/2014/494387 Text en Copyright © 2014 Jing Zhang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Jing Li, Da Hu, Weiwei Chen, Zhihua Yuan, Yubo Multilabel Image Annotation Based on Double-Layer PLSA Model |
title | Multilabel Image Annotation Based on Double-Layer PLSA Model |
title_full | Multilabel Image Annotation Based on Double-Layer PLSA Model |
title_fullStr | Multilabel Image Annotation Based on Double-Layer PLSA Model |
title_full_unstemmed | Multilabel Image Annotation Based on Double-Layer PLSA Model |
title_short | Multilabel Image Annotation Based on Double-Layer PLSA Model |
title_sort | multilabel image annotation based on double-layer plsa model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4066723/ https://www.ncbi.nlm.nih.gov/pubmed/24999490 http://dx.doi.org/10.1155/2014/494387 |
work_keys_str_mv | AT zhangjing multilabelimageannotationbasedondoublelayerplsamodel AT lida multilabelimageannotationbasedondoublelayerplsamodel AT huweiwei multilabelimageannotationbasedondoublelayerplsamodel AT chenzhihua multilabelimageannotationbasedondoublelayerplsamodel AT yuanyubo multilabelimageannotationbasedondoublelayerplsamodel |