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Learning latent semantic model with visual consistency for image analysis

文献类型：期刊论文

作者Cheng, Jian1; Li, Peng1; Rui, Ting2; Lu, Hanqing1
刊名MULTIMEDIA TOOLS AND APPLICATIONS
出版日期2015-02-01
卷号74期号:4页码:1341-1356
关键词PLSA Latent semantic model Image clustering
英文摘要Latent semantic models (e.g. PLSA and LDA) have been successfully used in document analysis. In recent years, many of the latent semantic models have also been proved to be promising for visual content analysis tasks, such as image clustering and classification. The topics and words which are two of the key components in latent semantic models have explicit semantic meaning in document analysis. However, these topics and words are difficult to be described or represented in visual content analysis tasks, which usually leads to failure in practice. In this paper, we consider simultaneously the topic consistency and word consistency in semantic space to adapt the traditional PLSA model to the visual content analysis tasks. In our model, the a"" (1)-graph is constructed to model the local neighborhood structure of images in feature space and the word co-occurrence is computed to capture the local word consistency. Then, the local information is incorporated into the model for topic discovering. Finally, the generalized EM algorithm is used to estimate the parameters. Extensive experiments on publicly available databases demonstrate the effectiveness of our approach.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
关键词[WOS]NONLINEAR DIMENSIONALITY REDUCTION ; REPRESENTATION
收录类别SCI
语种英语
WOS记录号WOS:000349356300010
公开日期2015-09-22
源URL[http://ir.ia.ac.cn/handle/173211/8057]  
专题自动化研究所_模式识别国家重点实验室_图像与视频分析团队
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.PLA Univ Sci & Technol, Nanjing 210007, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714		 Cheng, Jian,Li, Peng,Rui, Ting,et al. Learning latent semantic model with visual consistency for image analysis[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2015,74(4):1341-1356.
APA Cheng, Jian,Li, Peng,Rui, Ting,&Lu, Hanqing.(2015).Learning latent semantic model with visual consistency for image analysis.MULTIMEDIA TOOLS AND APPLICATIONS,74(4),1341-1356.
MLA Cheng, Jian,et al."Learning latent semantic model with visual consistency for image analysis".MULTIMEDIA TOOLS AND APPLICATIONS 74.4(2015):1341-1356.

入库方式： OAI收割

来源：自动化研究所

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