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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/494387 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832554505065988096 |
|---|---|
| author | Jing Zhang Da Li Weiwei Hu Zhihua Chen Yubo Yuan |
| author_facet | Jing Zhang Da Li Weiwei Hu Zhihua Chen Yubo Yuan |
| author_sort | Jing Zhang |
| collection | DOAJ |
| 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 | Article |
| id | doaj-art-874765bbcd904656b8d35e28ec546452 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-874765bbcd904656b8d35e28ec5464522025-02-03T05:51:21ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/494387494387Multilabel Image Annotation Based on Double-Layer PLSA ModelJing Zhang0Da Li1Weiwei Hu2Zhihua Chen3Yubo Yuan4School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaDue 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.http://dx.doi.org/10.1155/2014/494387 |
| spellingShingle | Jing Zhang Da Li Weiwei Hu Zhihua Chen Yubo Yuan Multilabel Image Annotation Based on Double-Layer PLSA Model The Scientific World Journal |
| 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 |
| url | http://dx.doi.org/10.1155/2014/494387 |
| work_keys_str_mv | AT jingzhang multilabelimageannotationbasedondoublelayerplsamodel AT dali multilabelimageannotationbasedondoublelayerplsamodel AT weiweihu multilabelimageannotationbasedondoublelayerplsamodel AT zhihuachen multilabelimageannotationbasedondoublelayerplsamodel AT yuboyuan multilabelimageannotationbasedondoublelayerplsamodel |