Support Vector Machine Concept-Dependent Active Learning for Image Retrieval
Edward Yi Chang, Simon Tong, Chengwei Chang · 2005
Relevance feedback is a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively learns a user’s desired output or query concept by asking the user whether certain proposed images are relevant or not. For a learning algorithm to be effective, it must learn a user’s query concept accurately and quickly, while also asking the user to label only a small number of images. In addition, the concept-learning algorithm should consider the complexity a concept in determining its learning strategies. In this paper, we present the use of support vector machines active learning in a concept-dependent way (SVM CD Active) for conducting relevance feedback. We characterize a concept’s complexity using three measures: hit-rate, isolation and diversity. To reduce concept complexity so as to improve concept learnability, we propose a multimodal learning approach that uses images ’ semantic labels to intelligently adjust the sampling strategy and the sampling pool of SVM CD Active. Our empirical study on several datasets shows that active learning outperforms traditional passive learning, and concept-dependent learning is superior to the traditional conceptindependent relevance-feedback schemes.