PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Randomized Clustering Forests for Building Fast and Discriminative Visual Vocabularies
Frank Moosmann, William Triggs and Frederic Jurie
In: NIPS ' 06(2006).

Abstract

Some of the most effective recent methods for content-based image classification work by extracting dense or sparse local image descriptors, quantizing them according to a coding rule such as k-means vector quantization, accumulating histograms of the resulting “visual word” codes over the image, and classifying these with a conventional classifier such as an SVM. Large numbers of descriptors and large codebooks are needed for good results and this becomes slow using k-means. We introduce Extremely Randomized Clustering Forests – ensembles of randomly created clustering trees – and show that these provide more accurate results, much faster training and testing and good resistance to background clutter in several state-of-the-art image classification tasks.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:2438
Deposited By:Frederic Jurie
Deposited On:22 November 2006