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
GLSLIM模型(Global and Local SLIM)是基于SLIM模型(Sparse Linear Methods)并优于SLIM模型的推荐算法。它将GLOBAL模型和LOCAL模型结合,GLOBAL模型用来捕获物品在所有用户之间的相似性,LOCAL模型用来获取物品在某个用户子集中的相似性...GLSLIM模型(Global and Local SLIM)是基于SLIM模型(Sparse Linear Methods)并优于SLIM模型的推荐算法。它将GLOBAL模型和LOCAL模型结合,GLOBAL模型用来捕获物品在所有用户之间的相似性,LOCAL模型用来获取物品在某个用户子集中的相似性,通过两种模型的结合可以进一步优化用户的个性化推荐。但该算法存在天然缺陷,就是被用户评价或购买过的物品之间的相似度才可以被学习到,没有被购买过的物品之间的相似度为0。这将导致用户购买过的相似物品才有机会被推荐,相似度为0的物品无法推荐给用户。为了改善这种情况,利用一种PLSA模型解决这个问题,基于两种模型的组合进行协同过滤推荐。实验结果表明,虽然推荐结果的准确性略微降低,但是能挖掘用户的潜在兴趣。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
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。
文摘GLSLIM模型(Global and Local SLIM)是基于SLIM模型(Sparse Linear Methods)并优于SLIM模型的推荐算法。它将GLOBAL模型和LOCAL模型结合,GLOBAL模型用来捕获物品在所有用户之间的相似性,LOCAL模型用来获取物品在某个用户子集中的相似性,通过两种模型的结合可以进一步优化用户的个性化推荐。但该算法存在天然缺陷,就是被用户评价或购买过的物品之间的相似度才可以被学习到,没有被购买过的物品之间的相似度为0。这将导致用户购买过的相似物品才有机会被推荐,相似度为0的物品无法推荐给用户。为了改善这种情况,利用一种PLSA模型解决这个问题,基于两种模型的组合进行协同过滤推荐。实验结果表明,虽然推荐结果的准确性略微降低,但是能挖掘用户的潜在兴趣。
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.
