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Fusing PLSA model and Markov random fields for automatic image annotation
  • ISSN号：0372-2112
  • 期刊名称：《电子学报》
  • 时间：0
  • 分类：TP391.12[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] TN911.73[电子电信—通信与信息系统;电子电信—信息与通信工程]
  • 作者机构：[1]Institute of ComputerSoftware, Baoji University of Arts and Sciences, Baoji 721007, P. R. China, [2]Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China
  • 相关基金：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） and the National Science and Technology Support Program of China （No.2012BA107B02）.
作者： 田东平[1,2], Zhao Xiaofei[2], Shi Zhongzhi[2]
关键词： 马尔可夫随机场, 自动标注方法, 自动图像, 模型, 潜在语义分析, 字段, 联合概率, 语义概念, automatic image annotation, probabilistic latent semantic analysis （PLSA）, expectation maximization, Markov random fields （MRF）, image retrieval
中文摘要：

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

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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期刊信息
  • 《电子学报》
  • 中国科技核心期刊
  • 主管单位:中国科学技术协会
  • 主办单位:中国电子学会
  • 主编：郝跃
  • 地址：北京165信箱
  • 邮编：100036
  • 邮箱：new@ejournal.org.cn
  • 电话：010-68279116 68285082
  • 国际标准刊号：ISSN：0372-2112
  • 国内统一刊号：ISSN：11-2087/TN
  • 邮发代号:2-891
  • 获奖情况:
  • 2000年获国家期刊奖,2000年获国家自然科学基金志项基金支持,中国期刊方阵“双高”期刊
  • 国内外数据库收录:
  • 美国化学文摘（网络版）,荷兰文摘与引文数据库,美国工程索引,美国剑桥科学文摘,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,英国英国皇家化学学会文摘,中国北大核心期刊（2000版）
  • 被引量:57611