| Tian Dongping（田东平）,Zhao Xiaofei,Shi Zhongzhi.[J].高技术通讯(英文),2014,20(4):409~414 |
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| Fusing PLSA model and Markov random fields for automatic image annotation |
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| DOI：10.3772/j.issn.1006-6748.2014.04.011 |
| 中文关键词: |
| 英文关键词: automatic image annotation, probabilistic latent semantic analysis (PLSA), expectation maximization, Markov random fields (MRF), image retrieval |
| 基金项目: |
| Author Name | Affiliation | | Tian Dongping（田东平） | | | Zhao Xiaofei | | | Shi Zhongzhi | |
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| 中文摘要: |
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| 英文摘要: |
| A novel image auto-annotation method is presented based on probabilistic latent semantic analysis (PLSA) model and multiple Markov random fields (MRF). A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts, then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the image. The novelty of our method mainly lies in two aspects: exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image annotation. To demonstrate the effectiveness of this approach, an experiment on the Corel5k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches. |
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