0


在基线之间阅读:使用图像标签中的隐式线索进行对象定位

Reading Between The Lines: Object Localization Using Implicit Cues from Image Tags
课程网址: http://videolectures.net/cvpr2010_hwang_rbtl/  
主讲教师: Sung Ju Hwang
开课单位: 德克萨斯大学
开课时间: 2010-07-19
课程语种: 英语
中文简介:
标记图像的当前使用通常仅利用最明确的信息:名词与图像中某处的对象之间的链接。我们建议利用“未说出”的线索,这些线索位于图像标签的无序列表中,以便改善对象的本地化。我们从图像标记中定义了三个新颖的隐含特征 - 每个对象的相对突出性由其提及的顺序表示,比例约束暗示未命名的对象,以及列表中名称附近暗示的松散空间链接。通过在给定这些线索的情况下学习条件密度超过定位参数(位置和比例),我们展示了如何在检测标记对象时提高机器人精度和效率。我们使用PASCAL VOC和LabelMe数据集中的25个对象类别验证我们的方法,并证明相对于传统的滑动窗口以及视觉上下文基线。
课程简介: Current uses of tagged images typically exploit only the most explicit information: the link between the nouns named and the objects present somewhere in the image. We propose to leverage “unspoken” cues that rest within an ordered list of image tags so as to improve object localization. We define three novel implicit features from an image’s tags—the relative prominence of each object as signified by its order of mention, the scale constraints implied by unnamed objects, and the loose spatial links hinted by the proximity of names on the list. By learning a conditional density over the localization parameters (position and scale) given these cues, we show how to improve both accuracy and efficiency when detecting the tagged objects. We validate our approach with 25 object categories from the PASCAL VOC and LabelMe datasets, and demonstrate its effectiveness relative to both traditional sliding windows as well as a visual context baseline.
关 键 词: 标记图像; 无序列表; 图像标签
课程来源: 视频讲座网
最后编审: 2020-06-10:yumf
阅读次数: 24