Image compression in real-time multiprocessor systems using divisive K-means clustering
Dmitriy Fradkin, Ilya B. Muchnik, Simon Streltsov · 2004
In recent years, clustering became one of the fundamental methods of large dataset analysis. In particular, clustering is an important component of real-time image compression and exploitation algorithms, such as vector quantization, segmentation of SAR, EO/IR, and hyperspectral imagery, group tracking, and behavior pattern analysis. Thus, development of fast scalable real-time clustering algorithms is important to enable exploitation of imagery coming from surveillance and reconnaissance airborne platforms. Clustering methods are widely used in pattern recognition, data compression, data mining, but the problem of using them in real-time systems has not been a focus of most algorithm designers. We describe a practical clustering procedure that is designed specifically for compression of 2D images and can satisfy stringent requirements of real-time onboard processing.