Adaptive Pattern Discovery for Interactive Multimedia Retrieval

13 years 8 months ago
Adaptive Pattern Discovery for Interactive Multimedia Retrieval
Relevance feedback has been an indispensable component for multimedia retrieval systems. In this paper, we present an adaptive pattern discovery method, which addresses relevance feedback by interactively discovering meaningful patterns of relevant objects. To facilitate pattern discovery, we first present a dynamic feature extraction method, which aims to alleviate the curse of dimensionality by extracting a feature subspace using balanced information gain. In the feature subspace, we train an online pattern classification method called adaptive random forests to classify multimedia objects as relevant or irrelevant. Our adaptive random forests adapts the traditional classification method known as random forests for relevance feedback. It improves the efficiency of pattern discovery by choosing the most-informative samples for online learning. Extensive experiments are carried out on a Corel image set (with 31,438 images) to evaluate the performance of our method as compared agai...
Yimin Wu, Aidong Zhang
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where CVPR
Authors Yimin Wu, Aidong Zhang
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