Fusing PLSA model and Markov random fields for automatic image annotation  

作　　者：田东平 Zhao Xiaofei Shi Zhongzhi 

机构地区：[1]Institute of Computer Software,Baoji University of Arts and Sciences [2]Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences

出　　处：《High Technology Letters》2014年第4期409-414,共6页高技术通讯（英文版）

基　　金：Supported by the National Basic Research Priorities Program(No.2013CB329502);the National High-tech R&D Program of China(No.2012AA011003);National Natural Science Foundation of China(No.61035003,61072085,60933004,60903141);the National Scienceand Technology Support Program of China(No.2012BA107B02)

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 Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.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.

关 键 词：automatic image annotation probabilistic latent semantic analysis （PLSA） expectation maximization Markov random fields （MRF） image retrieval 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]