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ICITA
2005
IEEE

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

13 years 9 months ago
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.
Charlie K. Dagli, ShyamSundar Rajaram, Thomas S. H
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where ICITA
Authors Charlie K. Dagli, ShyamSundar Rajaram, Thomas S. Huang
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