Stepwise Metric Adaptation Based on Semi-Supervised Learning for Boosting Image Retrieval Performance
نویسندگان
چکیده
For a specific set of features chosen for representing images, the performance of a content-based image retrieval (CBIR) system depends critically on the similarity measure used. Based on a recently proposed semisupervised metric learning method called locally linear metric adaptation (LLMA), we propose in this paper a stepwise LLMA algorithm for boosting the retrieval performance of CBIR systems by incorporating relevance feedback from users collected over multiple query sessions. Unlike most existing metric learning methods which learn a global Mahalanobis metric, the transformation performed by LLMA is more general in that it is linear locally but nonlinear globally. Moreover, the efficiency problem is well addressed by the stepwise LLMA algorithm. We also report experimental results performed on a real-world color image database to demonstrate the effectiveness of our method.
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