Reading Between The Lines: Object Localization Using Implicit Cues from Image Tags
published: July 19, 2010, recorded: June 2010, views: 301
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Download slides: cvpr2010_hwang_rbtl_01.v1.pdf (1.3 MB)
Download slides: cvpr2010_hwang_rbtl_01.ppt (8.2 MB)
Download article: cvpr2010_hwang_rbtl_01.pdf (1.6 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !