MULTI-LABEL SVM ACTIVE LEARNING FOR IMAGE CLASSIFICATION (TP-P6)

Author(s) : Xuchun Li (Nanyang Technological University, Singapore) Lei Wang (Nanyang Technological University, Singapore) Eric Sung (Nanyang Technological University, Singapore)

Abstract : 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. Hence, multi-label image classification problem arises. 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. Multi-label image classification also meets this problem. 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.

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