Adaptive image representation with segmented orthogonal matching pursuit

In this paper a novel algorithm for adaptive signal expansion is presented. Here the main concern is to efficiently represent the natural images and audio signals. These signals are one or two dimensional signals with unknown or time-varying characteristics. For this type of signal, linear expansion with a fixed set of basis functions is not flexible enough to represent the data with the desired degree of sparseness. We introduce a new algorithm called segmented orthogonal matching pursuit (SOMP). Our experimental results show that the SOMP algorithm is more suitable than the existing signal expansion algorithms for efficient representation of audio and visual information.