Multi-label image data is becoming ubiquitous. Image semantic understanding is typically formulated as a classification problem. This paper focuses on multi-label active learning for image classification. It first extends a traditional example based active learning method for multilabel active learning for image classification. Since the traditional example based active method doesn't work well, we propose a novel example-label based multi-label active learning method. Our experimental results on two image datasets demonstrate that the proposed method significantly reduces the labeling workload and improves the performance of the built classifier. Additionally, we conduct experiments on two other types of multi-label datasets for validating the versatility of our proposed method, and the experimental results show the consistent effect.