| Tian Dongping（田东平）.[J].高技术通讯(英文),2015,21(1):78~84 |
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| Exploiting PLSA model and conditional random field for refining image annotation |
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| DOI：10.3772/j.issn.1006-6748.2015.01.011 |
| 中文关键词: |
| 英文关键词: automatic image annotation, probabilistic latent semantic analysis（PLSA）, expectation-maximization, conditional random field（CRF）, Flickr distance, image retrieval |
| 基金项目: |
| Author Name | Affiliation | | Tian Dongping（田东平） | |
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| 中文摘要: |
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| 英文摘要: |
| This paper presents a new method for refining image annotation by integrating probabilistic latent semantic analysis (PLSA) with conditional random field (CRF). First a PLSA model with asymmetric modalities is constructed to predict a candidate set of annotations with confidence scores, and then model semantic relationship among the candidate annotations by leveraging conditional random field. In CRF, the confidence scores generated by the PLSA model and the Flickr distance between pairwise candidate annotations are considered as local evidences and contextual potentials respectively. The novelty of our method mainly lies in two aspects: exploiting PLSA to predict a candidate set of annotations with confidence scores as well as CRF to further explore the semantic context among candidate annotations for precise image annotation. To demonstrate the effectiveness of the method proposed in this paper, an experiment is conducted on the standard Corel dataset and its results are compared favorably with several state-of-the-art approaches. |
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