Harvesting weakly-tagged images for computer vision tasks

摘要

To crawl large amounts of weakly-tagged images for computer vision tasks such as object detection and scene recognition, it is very important to develop new techniques for tag cleansing and word sense disambiguation (i.e., removing irrelevant images from the crawled results). Based on this observation, a topic network is first generated to characterize both the semantic similarity contexts and the visual similarity contexts between the image topics more sufficiently. The topic network is used to represent the classes of objects and scenes of interest. Second, both the visual similarity contexts between the images and the semantic similarity contexts between their tags are integrated for tag cleansing and word sense disambiguation. By addressing the issues of polysemes and synonyms more effectively, our word sense disambiguation algorithm can determine the relevance between the images and the associated tags more precisely, and thus it can allow us to crawl large-scale weakly-tagged images for computer vision tasks.