Semantic-gap-oriented active learning for multilabel image annotation

User interaction is an effective way to handle the semantic gap problem in image annotation. To minimize user effort in the interactions, many active learning methods were proposed. These methods treat the semantic concepts individually or correlatively. However, they still neglect the key motivatio...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 21(2012), 4 vom: 21. Apr., Seite 2354-60
1. Verfasser: Tang, Jinhui (VerfasserIn)
Weitere Verfasser: Zha, Zheng-Jun, Tao, Dacheng, Chua, Tat-Seng
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2012
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Letter Research Support, Non-U.S. Gov't
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520 |a User interaction is an effective way to handle the semantic gap problem in image annotation. To minimize user effort in the interactions, many active learning methods were proposed. These methods treat the semantic concepts individually or correlatively. However, they still neglect the key motivation of user feedback: to tackle the semantic gap. The size of the semantic gap of each concept is an important factor that affects the performance of user feedback. User should pay more efforts to the concepts with large semantic gaps, and vice versa. In this paper, we propose a semantic-gap-oriented active learning method, which incorporates the semantic gap measure into the information-minimization-based sample selection strategy. The basic learning model used in the active learning framework is an extended multilabel version of the sparse-graph-based semisupervised learning method that incorporates the semantic correlation. Extensive experiments conducted on two benchmark image data sets demonstrated the importance of bringing the semantic gap measure into the active learning process 
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650 4 |a Research Support, Non-U.S. Gov't 
700 1 |a Zha, Zheng-Jun  |e verfasserin  |4 aut 
700 1 |a Tao, Dacheng  |e verfasserin  |4 aut 
700 1 |a Chua, Tat-Seng  |e verfasserin  |4 aut 
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