Active Context-Based Concept Fusionwith Partial User Labels

A new framework, called active context-based concept fusion, is proposed for effectively improving the accuracy of semantic concept detection in images and videos, by using information theoretic criteria to automatically determine the optimal concepts for user annotation.

Abstract

In this paper we propose a new framework, called active context-based concept fusion, for effectively improving the accuracy of semantic concept detection in images and videos. Our approach solicits user annotations for a small number of concepts, which are used to refine the detection of the rest of concepts. In contrast with conventional methods, our approach is active, by using information theoretic criteria to automatically determine the optimal concepts for user annotation. Our experiments over TRECVID 2005 development set (about 80 hours) show significant performance gains. In addition, we have developed an effective method to predict concepts that may benefit from context-based fusion.