Multiple-Instance Learning （MIL） is used to predict the unlabeled bags＇ label by learning the labeled positive training bags and negative training bags.Each bag is made up of several unlabeled instances.A bag is la...Multiple-Instance Learning （MIL） is used to predict the unlabeled bags＇ label by learning the labeled positive training bags and negative training bags.Each bag is made up of several unlabeled instances.A bag is labeled positive if at least one of its instances is positive,otherwise negative.Existing multiple-instance learning methods with instance selection ignore the representative degree of the selected instances.For example,if an instance has many similar instances with the same label around it,the instance should be more representative than others.Based on this idea,in this paper,a multiple-instance learning with instance selection via constructive covering algorithm （MilCa） is proposed.In MilCa,we firstly use maximal Hausdorff to select some initial positive instances from positive bags,then use a Constructive Covering Algorithm （CCA） to restructure the structure of the original instances of negative bags.Then an inverse testing process is employed to exclude the false positive instances from positive bags and to select the high representative degree instances ordered by the number of covered instances from training bags.Finally,a similarity measure function is used to convert the training bag into a single sample and CCA is again used to classification for the converted samples.Experimental results on synthetic data and standard benchmark datasets demonstrate that MilCa can decrease the number of the selected instances and it is competitive with the state-of-the-art MIL algorithms.展开更多
提出一种基于多示例学习的图像表示方法,将图像作为多示例包,用高斯滤波器将图像滤波并取样为由颜色区域构成的矩阵,使用单颜色及相邻区域(single blob with neighbors)的包生成方法。根据用户选择的实例图像生成正包和负包,使用MIL-SVD...提出一种基于多示例学习的图像表示方法,将图像作为多示例包,用高斯滤波器将图像滤波并取样为由颜色区域构成的矩阵,使用单颜色及相邻区域(single blob with neighbors)的包生成方法。根据用户选择的实例图像生成正包和负包,使用MIL-SVDD_I和MIL-SVDD_B算法进行实验。实验表明该图像表示方法是可行的。展开更多
文摘提出了一种基于图像显著点特征进行多示例学习(Multiple-instance learning)的图像检索方法。该方法对图像进行小波分解并跟踪不同尺度小波系数提取图像显著点;然后利用显著点特征进行检索,并在相关反馈中将图像看作多示例包,通过期望最大多样性密度(EM-DD,expectation maximization diverse density)方法进行多示例学习,获得体现图像语义的目标特征。在Corel和SIVAL两个图像库进行实验,结果表明该方法明显提高了检索的准确性。
基金supported by the National Natural Science Foundation of China (No. 61175046)the Provincial Natural Science Research Program of Higher Education Institutions of Anhui Province (No. KJ2013A016)+1 种基金the Outstanding Young Talents in Higher Education Institutions of Anhui Province (No. 2011SQRL146)the Recruitment Project of Anhui University for Academic and Technology Leader
文摘Multiple-Instance Learning （MIL） is used to predict the unlabeled bags＇ label by learning the labeled positive training bags and negative training bags.Each bag is made up of several unlabeled instances.A bag is labeled positive if at least one of its instances is positive,otherwise negative.Existing multiple-instance learning methods with instance selection ignore the representative degree of the selected instances.For example,if an instance has many similar instances with the same label around it,the instance should be more representative than others.Based on this idea,in this paper,a multiple-instance learning with instance selection via constructive covering algorithm （MilCa） is proposed.In MilCa,we firstly use maximal Hausdorff to select some initial positive instances from positive bags,then use a Constructive Covering Algorithm （CCA） to restructure the structure of the original instances of negative bags.Then an inverse testing process is employed to exclude the false positive instances from positive bags and to select the high representative degree instances ordered by the number of covered instances from training bags.Finally,a similarity measure function is used to convert the training bag into a single sample and CCA is again used to classification for the converted samples.Experimental results on synthetic data and standard benchmark datasets demonstrate that MilCa can decrease the number of the selected instances and it is competitive with the state-of-the-art MIL algorithms.
文摘提出一种基于多示例学习(Multiple-instance learning)的图像检索方法,将多示例学习应用于图像检索中,以有效的处理图像的歧义性。该方法首先将图像作为多示例包,其次采用自适应k-means图像分割算法将图像自动分成多个示例,然后根据用户选择的实例图像生成正包和反包,再采用EM-DD(expectation maximization diverse density)算法进行多示例学习,实现图像检索和相关反馈,最终使用户得到比较满意的结果。
文摘提出一种基于多示例学习的图像表示方法,将图像作为多示例包,用高斯滤波器将图像滤波并取样为由颜色区域构成的矩阵,使用单颜色及相邻区域(single blob with neighbors)的包生成方法。根据用户选择的实例图像生成正包和负包,使用MIL-SVDD_I和MIL-SVDD_B算法进行实验。实验表明该图像表示方法是可行的。
