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2007

Hyperplane Queries in a Feature-Space M-tree for Speeding up Active Learning

9 years 11 months ago
Hyperplane Queries in a Feature-Space M-tree for Speeding up Active Learning
In content-based retrieval, relevance feedback (RF) is a noticeable method for reducing the “semantic gap” between the low-level features describing the content and the usually higher-level meaning of user’s target. While recent RF methods based on active learning are able to identify complex target classes after relatively few iterations, they can be quite slow on very large databases. To address this scalability issue for active RF, we put forward a method that consists in the construction of an M-tree in the feature space associated to a kernel function and in performing approximate kNN hyperplane queries with this feature space M-tree. The experiments performed on two image databases show that a significant speedup can be achieved, at the expense of a limited increase in the number of feedback rounds.
Michel Crucianu, Daniel Estevez, Vincent Oria, Jea
Added 29 Oct 2010
Updated 07 Dec 2010
Type Conference
Year 2007
Where BDA
Authors Michel Crucianu, Daniel Estevez, Vincent Oria, Jean-Philippe Tarel
http://perso.lcpc.fr/tarel.jean-philippe/publis/bda07.html
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