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