DOES THE ENERGY SPECTRUM FROM GABOR WAVELET FILTERING REPRESENT SUFFICIENT INFORMATION FOR NEURAL NETWORK RECOGNITION AND CLASSIFICATION TASKS?

Recent results from neurophysiological studies [11] suggest that energy spectra (i.e., the square of the amplitude spectrum) could be a suitable way to simulate, in a physiologically plausible manner, the spectral integration of sensory neurons. In this paper, we show for a high-level cognition task, the adequateness of the energy spectrum as an image descriptor for neural network computations. We used a simulation of cortical complex cell functions as a perceptual model which extracts image information. In a first simulation, we tested the energy spectrum descriptors with a back-propagation auto-encoder. In a second simulation, we tested the same descriptors with a standard back-propagation heteroassociator. The results show a reliable ability of these two types of neural networks to categorize and to generalize prior training to new exemplars based on the information provided by the energy spectrum of natural scene images.