Classification decision tree algorithms have recently been used in pattern-recognition problems. In this paper, we propose a self-designing system that uses the classification tree algorithms and that is capable of recognizing a large number of signals. Preprocessing techniques are used to make the recognition process more effective. A combination of the original, as well as the preprocessed, signals is projected into different transform domains. Enormous sets of criteria that characterize the signals can be developed from the signal representations in these domains. At each node of the classification tree, an appropriately selected criterion is optimized with respect to desirable performance features such as complexity and noise immunity. The criterion is then employed in conjunction with a vector quantizer to divide the signals presented at a particular node in that stage into two approximately equal groups. When the process is complete, each signal is represented by a unique composite binary word index, which corresponds to the signal path through the tree, from the input to one of the terminal nodes of the tree. Experimental results verify the excellent classification accuracy of this system. High performance is maintained for both noisy and corrupt data.
© 2004 Optical Society of America
(070.5010) Fourier optics and signal processing : Pattern recognition
(100.0100) Image processing : Image processing
(100.7410) Image processing : Wavelets
(200.3050) Optics in computing : Information processing
(200.4260) Optics in computing : Neural networks
Manal M. Abdelwahab and Wasfy B. Mikhael, "Multistage Classification and Recognition that Employs Vector Quantization Coding and Criteria Extracted from Nonorthogonal and Preprocessed Signal Representations," Appl. Opt. 43, 416-424 (2004)