Generalized Histogram: Empirical Optimization of Low Dimensional Features for Image Matching

We propose Generalized Histogram as low-dimensional representation of an image for efficient and precise image matching. Multiplicity detection of videos in broadcast video archives is getting important for many video-based applications including commercial film identification, unsupervised video parsing and structuring, and robust highlight shot detection. This inherently requires efficient and precise image matching among extremely huge number of images. Histogram-based image similarity search and matching is known to be effective, and its enhancement techniques such as adaptive binning, subregion histogram, and adaptive weighting have been studied. We show that these techniques can be represented as linear conversion of high-dimensional primitive histograms and can be integrated into generalized histograms. A linear learning method to obtain generalized histograms from sample sets is presented with a sample expansion technique to circumvent the overfitting problem due to high-dimensionality and insufficient sample size. The generalized histogram takes advantage of these techniques, and achieves more than 90% precision and recall with 16-D generalized histogram compared to the ground truth computed by normalized cross correlation. The practical importance of the work is revealed by successful matching performance with 20,000 frame images obtained from actual broadcast videos.