Combining Diversity-Based Active Learning with Discriminant Analysis in Image Retrieval

Small-sample learning in image retrieval is a pertinent and interesting problem. Relevance feedback is an active area of research that seeks to find algorithms that are robust with only a small number of examples. Much work has been done in both the machine learning and pattern recognition communities to develop algorithms that learn a highlevel semantic concept in a low-level image feature space. In this paper we seek to leverage techniques from both these communities to explore a hybrid relevance feedback system which combines the insight gained from discriminant analysis and active learning. Our technique uses a diversitybased pool-query technique along with biased discriminant analysis to improve the query refinement process. Comparative results are observed and thoughts for future work are presented.

• Type: Conference
• Year: 2005
• Where: ICITA
• Authors: Charlie K. Dagli, ShyamSundar Rajaram, Thomas S. Huang
• Keywords: Discriminant Analysis, Hybrid Relevance Feedback, ICITA 2005, Information Technology, Relevance Feedback