ㆍ발행기관 : 한국인터넷정보학회ㆍ수록지정보 : KSII Transactions on Internet and Information Systems (TIIS) / 6권 / 10호 ㆍ저자명 : ( Minxian Li ) , ( Jinhui Tang ) , ( Chunxia Zhao )
Due to the semantic gap issue, the performance of automatic image annotation is still far from satisfactory. Active learning approaches provide a possible solution to cope with this problem by selecting most effective samples to ask users to label for training. One of the key research points in active learning is how to select the most effective samples. In this paper, we propose a novel active learning approach based on sparse graph. Comparing with the existing active learning approaches, the proposed method selects the samples based on two criteria: uncertainty and representativeness. The representativeness indicates the contribution of a sample`s label propagating to the other samples, while the existing approaches did not take the representativeness into consideration. Extensive experiments show that bringing the representativeness criterion into the sample selection process can significantly improve the active learning effectiveness.