Image-content classification using a dynamically allocated ALISA texture module

ALISA (adaptive learning image and signal analysis) is an adaptive learning image and signal classification engine based on the collective learning systems theory. Using supervised training, the ALISA engine builds a set of multidimensional feature histograms that estimate the joint probability density function of the feature space for each trained class. Six general-purpose features, one with a precision of 60 bins and the rest with 20 bins, were used to build a dynamically allocated sparse data structure instead of a complete static structure for each class. During the training of the new dynamically allocated ALISA with 6 different classes (sky, water, skin, rose, evergreen, and grass), a total about 12,000,000 counts were accumulated during training, generating fewer than 150,000 unique feature vectors. The results demonstrate the classification of several test images for each of the 6 trained classes. Much work remains to be done to optimize the new dynamically allocated ALISA classifier, but the initial results are encouraging.