Randomized Clustering Forests for Building Fast and Discriminative Visual Vocabularies
Frank Moosmann, William Triggs and Frederic Jurie
In: NIPS 2006, Dec 2006, Vancouver.

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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