Optimization on active learning strategy for object category retrieval

David Gorisse 1 Matthieu Cord 2 Frédéric Precioso 1
1 MIDI - Multimedia Indexation and Data Integration
ETIS - Equipes Traitement de l'Information et Systèmes
2 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Active learning is a machine learning technique which has attracted a lot of research interest in the content-based image retrieval (CBIR) in recent years. To be effective, an active learning system must be fast and efficient using as few (relevance) feedback iterations as possible. Scalability is the major problem for such an on-line learning method, since the complexity of such methods on a database of size n is in the best case O(n * log(n)). In this article we propose a strategy to overcome this limitation. Our technique exploits ultra fast retrieval methods like Locality Sensitive Hashing (LSH), recently applied for unsupervised image retrieval. Combined with active selection, our method is able to achieve very fast active learning task in very large database. Experiments on VOC2006 database are reported, results are obtained four times faster while preserving the accuracy.
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Conference papers
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Submitted on : Monday, January 14, 2013 - 11:53:05 AM
Last modification on : Friday, October 4, 2019 - 12:14:02 PM

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David Gorisse, Matthieu Cord, Frédéric Precioso. Optimization on active learning strategy for object category retrieval. 16th IEEE International Conference on Image Processing (ICIP 09), Nov 2009, Cairo, Egypt. pp.1873-1876, ⟨10.1109/ICIP.2009.5413554⟩. ⟨hal-00773561⟩

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