Multiple description coding models/multiple description sampling-based multiple classifier systems and its application to automatic target recognition
Widhyakorn Asdornwised, SOMCHAI JITAPUNKUL · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
In this paper, we propose a new multiple classifier system (MCS) based on two concatenated stages of multiple description coding models (MDC) and multiple description sampling (MDS). This paper draws on concepts coming from a variety of disciplines that includes classical concatenated coding of error correcting codes, multiple description coding of wavelet based image compression, Adaboost and importance sampling of multiple classifier systems, and antithetic-common varaites of Monte Carlo Methods. In our previous work, we proposed and extended several methods in MDC to MCS with inspirations from two frameworks. First, we found that one of our methods is equivalent to one of the variance reduction techniques, called antithetic-common variates, in the Monte Carlo Methods (MCM). Having established that Adaboost can be interpreted as important sampling in MCM, and it can directly be interpreted as MDC, we define the term "multiple description sampling (MDS)" for Adaboost. Second, another equivalent relation between one of our methods and transmitting data over heterogeneous network, especially wireless networks, are established. One of the benefits of our approach is that it allows us to formulate a generalized class of signal processing based weak classification algorithms. This will be very applicable for MDC-MDS in high dimensional classification problems, such as image/target recognition. Performance results for automatic target recognition are presented for synthetic aperture radar (SAR) images from the MSTAR public release data set. From the experimental results, our proposed method outperform state-of-the-art multiple classifier systems, such as Adaboost and SVM-ECOC etc.