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Multi-label SVM active learning for image classification

12 years 11 months ago
Multi-label SVM active learning for image classification
Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categorized into multiple classes simultaneously. Multi-label image classification focuses on the problem that each image can have one or multiple labels. It is known that manually labelling images is time-consuming and expensive. In order to reduce the human effort of labelling images, especially multi-label images, we proposed a multi-label SVM active learning method. We also proposed two selection strategies: Max Loss strategy and Mean Max Loss strategy. Experimental results on both artificial data and real-world images demonstrated the advantage of proposed method.
Xuchun Li, Lei Wang, Eric Sung
Added 24 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2004
Where ICIP
Authors Xuchun Li, Lei Wang, Eric Sung
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