传统的视觉词典模型(Bag of Visual Words Model,Bo VWM)中广泛存在视觉单词同义性和歧义性问题.且视觉词典中的一些噪声单词—"视觉停用词",也会降低视觉词典的语义分辨能力.针对这些问题,本文提出了基于近义词自适应软分配...传统的视觉词典模型(Bag of Visual Words Model,Bo VWM)中广泛存在视觉单词同义性和歧义性问题.且视觉词典中的一些噪声单词—"视觉停用词",也会降低视觉词典的语义分辨能力.针对这些问题,本文提出了基于近义词自适应软分配和卡方模型的图像目标分类方法.首先,该方法利用概率潜在语义分析模型(Probabilistic Latent Semantic Analysis,PLSA)分析图像中视觉单词的语义共生概率,挖掘图像隐藏的语义主题,进而得到语义主题在某一视觉单词上的概率分布;其次,引入K-L散度度量视觉单词间的语义相关性,获取语义相关的近义词;然后,结合自适应软分配策略实现SIFT特征点与若干语义相关的近义词之间的软映射;最后,利用卡方模型滤除"视觉停用词",重构视觉词汇分布直方图,并采用SVM分类器完成目标分类.实验结果表明,新方法能够有效克服视觉单词同义性和歧义性问题带来的不利影响,增强视觉词典的语义分辨能力,较好地改善了目标分类性能.展开更多
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
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
The global view of firewall policy conflict is important for administrators to optimize the policy.It has been lack of appropriate firewall policy global conflict analysis,existing methods focus on local conflict dete...The global view of firewall policy conflict is important for administrators to optimize the policy.It has been lack of appropriate firewall policy global conflict analysis,existing methods focus on local conflict detection.We research the global conflict detection algorithm in this paper.We presented a semantic model that captures more complete classifications of the policy using knowledge concept in rough set.Based on this model,we presented the global conflict formal model,and represent it with OBDD(Ordered Binary Decision Diagram).Then we developed GFPCDA(Global Firewall Policy Conflict Detection Algorithm) algorithm to detect global conflict.In experiment,we evaluated the usability of our semantic model by eliminating the false positives and false negatives caused by incomplete policy semantic model,of a classical algorithm.We compared this algorithm with GFPCDA algorithm.The results show that GFPCDA detects conflicts more precisely and independently,and has better performance.展开更多
文摘传统的视觉词典模型(Bag of Visual Words Model,Bo VWM)中广泛存在视觉单词同义性和歧义性问题.且视觉词典中的一些噪声单词—"视觉停用词",也会降低视觉词典的语义分辨能力.针对这些问题,本文提出了基于近义词自适应软分配和卡方模型的图像目标分类方法.首先,该方法利用概率潜在语义分析模型(Probabilistic Latent Semantic Analysis,PLSA)分析图像中视觉单词的语义共生概率,挖掘图像隐藏的语义主题,进而得到语义主题在某一视觉单词上的概率分布;其次,引入K-L散度度量视觉单词间的语义相关性,获取语义相关的近义词;然后,结合自适应软分配策略实现SIFT特征点与若干语义相关的近义词之间的软映射;最后,利用卡方模型滤除"视觉停用词",重构视觉词汇分布直方图,并采用SVM分类器完成目标分类.实验结果表明,新方法能够有效克服视觉单词同义性和歧义性问题带来的不利影响,增强视觉词典的语义分辨能力,较好地改善了目标分类性能.
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.
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.
基金supported by the National Nature Science Foundation of China under Grant No.61170295 the Project of National ministry under Grant No.A2120110006+2 种基金 the Co-Funding Project of Beijing Municipal Education Commission under Grant No.JD100060630 the Beijing Education Committee General Program under Grant No. KM201211232010 the National Nature Science Foundation of China under Grant NO. 61370065
文摘The global view of firewall policy conflict is important for administrators to optimize the policy.It has been lack of appropriate firewall policy global conflict analysis,existing methods focus on local conflict detection.We research the global conflict detection algorithm in this paper.We presented a semantic model that captures more complete classifications of the policy using knowledge concept in rough set.Based on this model,we presented the global conflict formal model,and represent it with OBDD(Ordered Binary Decision Diagram).Then we developed GFPCDA(Global Firewall Policy Conflict Detection Algorithm) algorithm to detect global conflict.In experiment,we evaluated the usability of our semantic model by eliminating the false positives and false negatives caused by incomplete policy semantic model,of a classical algorithm.We compared this algorithm with GFPCDA algorithm.The results show that GFPCDA detects conflicts more precisely and independently,and has better performance.
