Does the energy spectrum from Gabor wavelet filtering contain sufficient information for neural network recognition and classification tasks?
Results from neurophysiological studies (Gollisch & Herz, in press) suggest that the energy spectrum (i.e., the square of the amplitude spectrum) can be used to simulate in an appropriate physiological manner the spectral integration of sensory neurons. We have attempted to show the effectiveness of energy-spectum descriptors for neural network simulations of a high level cognitive task. We used a neurobiologically plausible simulation of the complex cells in the striate cortex as a perceptual model. This was done with a bank of Gabor wavelets applied in the Fourier domain in order to simulate mammalian visual processes (Jones & Palmer, 1987; Jones, Stepnoski & Palmer, 1987). The energy-value outputs of this perceptual model were presented to two different kinds of neural network classifiers and their respective performances were recorded for a recognition/classification task. The stimuli used correspond to 6 categories of 12 natural scene images: Beach, City, Forest, Mountain, Indoor
