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基于多实例学习的兴趣区域定位凸方法

A Convex Method for Locating Regions of Interest with Multi-Instance Learning
课程网址: http://videolectures.net/ecmlpkdd09_li_cmlrimil/  
主讲教师: Yu-Feng Li
开课单位: 南京大学
开课时间: 2009-10-20
课程语种: 英语
中文简介:
在基于内容的图像检索(CBIR)和图像筛选中,通常希望自动定位图像中的感兴趣区域(ROI)。这可以通过将每个图像视为一包实例(区域)来使用多实例学习技术来实现。许多基于SVM的方法成功地预测了袋标签。但是,很少有人可以找到投资回报率,而且往往是基于本地搜索或EM类型策略,这可能会陷入局部最小值。为了解决这个问题,我们在本文中提出了两种凸优化方法,它们分别通过实例级别和包级别中的密钥实例生成来最大化概念边界。而且,这可以通过切割平面算法有效地解决。实验表明,所提出的方法可以有效地定位ROI。此外,在基准数据集上,它们实现了与最先进算法竞争的性能。
课程简介: In content-based image retrieval (CBIR) and image screening, it is often desirable to automatically locate the regions of interest (ROI) in the images. This can be accomplished with multi-instance learning techniques by treating each image as a bag of instances (regions). Many SVM-based methods are successful in predicting the bag label. However, very few of them can locate the ROIs and often they are based on either the local search or an EM-type strategy, which may get stuck in local minima. To address this problem, we propose in this paper two convex optimization methods which maximize the margin of concepts via key instance generation in the instance-level and bag-level, respectively. Moreover, this can be efficiently solved with a cutting plane algorithm. Experiments show that the proposed methods can effectively locate ROIs. Moreover, on the benchmark data sets, they achieve performance that are competitive with state-of-the-art algorithms.
关 键 词: 图像检索; 图像筛选; 投资回报率
课程来源: 视频讲座网
最后编审: 2019-03-27:lxf
阅读次数: 37