Feature detection and fusion for intelligent compression

In Ducksbury´s previous work (2000) a novel approach was described, which used automatic target detection together with compression techniques to achieve intelligent compression by exploiting knowledge of the image content. In this paper a set of standard feature detectors such as HV-quadtrees, approximate entropy and phase congruency are used as target discriminators. These detectors all attempt to find potential areas of interest within an image but will be slightly different in their estimates. A probabilistic (Bayesian belief) network is then used to fuse this information into a single hypothesis of ´interesting areas´ within an image. A wavelet-based decomposition can then be applied to the image in which selective destruction of wavelet coefficients is performed outside the cued areas of interest prior to the encoding with a version of the progressive SPIHT encoder.