In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines＇ inability to meet the personal...In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines＇ inability to meet the personalized requirements of users, a novel method based on probabilistic latent semantic analysis （PLSA） is proposed to convert query-oriented web search to user-oriented web search. First, a user profile represented as a user＇ s topics of interest vector is created by analyzing the user＇ s click through data based on PLSA, then the user＇ s queries are mapped into categories based on the user＇ s preferences, and finally the result list is re-ranked according to the user＇ s interests based on the new proposed method named user-oriented PageRank （UOPR）. Experiments on real life datasets show that the user-oriented search system that adopts PLSA takes considerable consideration of user preferences and better satisfies a user＇ s personalized information needs.展开更多
针对场景分类问题,本文提出一种基于图像局部边缘区域的EILBP(Edge Improved Local Binary Pattern)视觉特征描述结合PLSA模型场景分类方法。EILBP视觉特征通过利用局部边缘区域的梯度、方向分布与特征的局部空间分布等信息对图像进行...针对场景分类问题,本文提出一种基于图像局部边缘区域的EILBP(Edge Improved Local Binary Pattern)视觉特征描述结合PLSA模型场景分类方法。EILBP视觉特征通过利用局部边缘区域的梯度、方向分布与特征的局部空间分布等信息对图像进行充分合理地描述。首先对场景图像边缘轮廓稠密采样,得到以稠密采样点为中心的图像局部边缘区域并提取区域的EILBP特征作为视觉词汇,对视觉词汇聚类形成视觉词汇表(码本);然后,用词袋(BOW,Bag-Of-Words)模型描述图像;最后,利用PLSA模型对图像的词袋模型进行潜在语义挖掘并用判定式KNN分类器进行场景分类,得到测试图像集合的混淆矩阵。在多类场景图像上的实验表明,本文所用的方法不需要对场景内容进行人工标注,具有较高的分类准确率,且对具有边缘轮廓的图像分类精度较高。展开更多
This paper presents a new method for refining image annotation by integrating probabilistic la- tent semantic analysis （PLSA） with conditional random field （CRF）. First a PLSA model with asymmetric modalities is c...This paper presents a new method for refining image annotation by integrating probabilistic la- tent 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 ran- dom field. In CRF, the confidence scores generated lay the PLSA model and the Fliekr distance be- tween pairwise candidate annotations are considered as local evidences and contextual potentials re- spectively. The novelty of our method mainly lies in two aspects ： exploiting PLSA to predict a candi- date 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 re- sults are ＇compared favorably with several state-of-the-art approaches.展开更多
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 esti...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.展开更多
基金The National Natural Science Foundation of China(No60573090,60673139)
文摘In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines＇ inability to meet the personalized requirements of users, a novel method based on probabilistic latent semantic analysis （PLSA） is proposed to convert query-oriented web search to user-oriented web search. First, a user profile represented as a user＇ s topics of interest vector is created by analyzing the user＇ s click through data based on PLSA, then the user＇ s queries are mapped into categories based on the user＇ s preferences, and finally the result list is re-ranked according to the user＇ s interests based on the new proposed method named user-oriented PageRank （UOPR）. Experiments on real life datasets show that the user-oriented search system that adopts PLSA takes considerable consideration of user preferences and better satisfies a user＇ s personalized information needs.
文摘针对场景分类问题,本文提出一种基于图像局部边缘区域的EILBP(Edge Improved Local Binary Pattern)视觉特征描述结合PLSA模型场景分类方法。EILBP视觉特征通过利用局部边缘区域的梯度、方向分布与特征的局部空间分布等信息对图像进行充分合理地描述。首先对场景图像边缘轮廓稠密采样,得到以稠密采样点为中心的图像局部边缘区域并提取区域的EILBP特征作为视觉词汇,对视觉词汇聚类形成视觉词汇表(码本);然后,用词袋(BOW,Bag-Of-Words)模型描述图像;最后,利用PLSA模型对图像的词袋模型进行潜在语义挖掘并用判定式KNN分类器进行场景分类,得到测试图像集合的混淆矩阵。在多类场景图像上的实验表明,本文所用的方法不需要对场景内容进行人工标注,具有较高的分类准确率,且对具有边缘轮廓的图像分类精度较高。
基金Supported by the National Basic Research Priorities Programme(No.2013CB329502)the National High Technology Research and Development Programme of China(No.2012AA011003)+1 种基金the Natural Science Basic Research Plan in Shanxi Province of China(No.2014JQ2-6036)the Science and Technology R&D Program of Baoji City(No.203020013,2013R2-2)
文摘This paper presents a new method for refining image annotation by integrating probabilistic la- tent 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 ran- dom field. In CRF, the confidence scores generated lay the PLSA model and the Fliekr distance be- tween pairwise candidate annotations are considered as local evidences and contextual potentials re- spectively. The novelty of our method mainly lies in two aspects ： exploiting PLSA to predict a candi- date 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 re- sults are ＇compared favorably with several state-of-the-art approaches.
基金Supported by the National Basic Research Priorities Program(No.2013CB329502)the National High-tech R&D Program of China(No.2012AA011003)+1 种基金National Natural Science Foundation of China(No.61035003,61072085,60933004,60903141)the National Scienceand Technology Support Program of China(No.2012BA107B02)
文摘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.
