Title :
Web Image Organization and Object Discovery by Actively Creating Visual Clusters through Crowdsourcing
Author :
Qi Chen ; Gang Wang ; Chew Lim Tan
Author_Institution :
Dept. of Comput. Sci., Nat. Univ. of Singapore, Singapore, Singapore
Abstract :
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.
Keywords :
Internet; image retrieval; image segmentation; pattern clustering; Web image organization; actively visual cluster creation; crowdsourcing; high quality visual clusters; human workers; image collection; image data sets; image segment collection; informative image selection; manual labeling; object category discovery; two-phase framework; Computers; Crowdsourcing; Image segmentation; Labeling; Measurement; Support vector machines; Visualization; active clustering; crowdsourcing; image organization; object discovery;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on
Conference_Location :
Athens
Print_ISBN :
978-1-4799-0227-9
DOI :
10.1109/ICTAI.2012.64