Web Image Organization and Object Discovery by Actively Creating Visual Clusters through Crowdsourcing

In this paper, we propose to organize web images by actively creating visual clusters via crowd sourcing. We develop a two-phase framework to efficiently and effectively combine computers and a large number of human workers to build high quality visual clusters. The first phase partitions an image collection into multiple clusters, the second phase refines each generated cluster independently. In both phases, informative images are selected by computers and manually labeled by the crowds to learn improved models. Our method can be naturally extended to discover object categories in a collection of image segments. Experimental results on several data sets demonstrate the promise of our developed approach on both web image organization and object discovery tasks.