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PAMI
2008

Randomized Clustering Forests for Image Classification

8 years 12 months ago
Randomized Clustering Forests for Image Classification
This paper introduces three new contributions to the problems of image classification and image search. First, we propose a new image patch quantization algorithm. Other competitive approaches require a large code book and the sampling of many local regions for accurate image description, at the expense of a prohibitive processing time. We introduce Extremely Randomized Clustering Forests--ensembles of randomly created clustering trees--that are more accurate, much faster to train and test, and more robust to background clutter compared to state-of-the-art methods. Second, we propose an efficient image classification method that combines ERC-Forests and saliency maps very closely with image information sampling. For a given image, a classifier builds a saliency map online, which it uses for classification. We demonstrate speed and accuracy improvement in several state-of-the-art image classification tasks. Finally, we show that our ERC-Forests are used very successfully for learning di...
Frank Moosmann, Eric Nowak, Frédéric
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2008
Where PAMI
Authors Frank Moosmann, Eric Nowak, Frédéric Jurie
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