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2008

Combining visual dictionary, kernel-based similarity and learning strategy for image category retrieval

9 years 11 months ago
Combining visual dictionary, kernel-based similarity and learning strategy for image category retrieval
This paper presents a search engine architecture, RETIN, aiming at retrieving complex categories in large image databases. For indexing, a scheme based on a two-step quantization process is presented to compute visual codebooks. The similarity between images is represented in a kernel framework. Such a similarity is combined with online learning strategies motivated by recent Machine-Learning developments such as Active Learning. Additionally, an offline supervised learning is embedded in the kernel framework, offering a real opportunity to learn semantic categories. Experiments with real scenario carried out from the Corel Photo database demonstrate the efficiency and the relevance of the RETIN strategy and its outstanding performances in comparison to up-to-date strategies. Key words: Multimedia Retrieval, Machine Learning, Kernel Functions, Quantization PACS:
Philippe Henri Gosselin, Matthieu Cord, Sylvie Phi
Added 26 Dec 2010
Updated 26 Dec 2010
Type Journal
Year 2008
Where CVIU
Authors Philippe Henri Gosselin, Matthieu Cord, Sylvie Philipp-Foliguet
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