Multilabel SVM active learning for image classification

Authors

Xuchun Li, Nanyang Technological University, Singapore
Lei Wang, Nanyang Technological University, SingaporeFollow
Eric Sung, Nanyang Technological University, Singapore

RIS ID

54418

Publication Details

Li, X., Wang, L. & Sung, E. (2004). Multilabel SVM active learning for image classification. International Conference on Image Processing (ICIP) (pp. 2207-2210). Australia: IEEE.

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. Multilabel 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 multilabel images, we proposed a multilabel 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.