Fast Discriminative
visual codebooks using randomized clustering forests
F. Jurie
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.