When examples are represented / encoded as histograms in a problem or dataset, the histogram intersection kernel (HIK) is proved to be very effective in both clustering and classification of such datasets. The libHIK software package provides utilities that perform HIK-based kernel clustering, SVM classification, and the generation of  bag-of-visual-words (BOV) representations of images.

There are four components within libHIK:

• (Since version 2.0) ICD (Intersection Coordinate Descent), a fast, reliable, and scalable HIK SVM solver.
• Only slightly slower than linear SVM
• Scales to millions of examples and tens of thousands of feature dimensions
• (Since version 2.05) support for exponential HIK.
• A kernel k-means clustering method that uses the Histogram Intersection Kernel (HIK);
• K-means and K-median clustering are also implemented in libHIK;
• A method that makes applying HIK SVM models extremely fast on histograms that are integers;
• We also provided ways to convert real-valued histograms to integer ones. Our experiments show that there are no obvious difference between these two – the integer version works better in many cases;
• Use the clustering and classification methods in the bag-of-visual-words (BOV) model;
• An implementation of the CENTRIST visual descriptor;
• Common visual codebook generation methods used in a BOV model , e.g. k-means or Gaussian Mixture Model, use the Euclidean distance to cluster features into visual code words. However, most popular visual descriptors are histograms of image measurements. It has been shown that the HIK is more effective than the Euclidean distance in supervised learning tasks with histogram features.
• In libHIK, we demonstrate that HIK can also be used in an unsupervised manner to significantly improve the generation of visual codebooks;
• In addition, we propose a one-class SVM formulation to create more effective visual code words which can achieve even higher accuracy.
• (Since version 2.06) The output values are normalized to the range [0, 1].