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.

• Type: Conference
• Year: 2004
• Where: ICIP
• Authors: Xuchun Li, Lei Wang, Eric Sung
• Keywords: Active Learning Method, ICIP 2004, Image Classification, Image Processing, Max Loss Strategy, Mean Max Loss, Real-world Images