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.
Image-content classification using a dynamically allocated ALISA texture module
2000-01-01
1207518 byte
Conference paper
Electronic Resource
English
Image-Content Classification Using a Dynamically Allocated ALISA Texture Model
British Library Conference Proceedings | 2000
|Face Detection and Eye Location Using a Modified ALISA Texture Module
British Library Conference Proceedings | 2001
|Active safety management systems for modular dynamically allocated capacity storage systems
European Patent Office | 2024
|ACTIVE SAFETY MANAGEMENT SYSTEMS FOR MODULAR DYNAMICALLY ALLOCATED CAPACITY STORAGE SYSTEMS
European Patent Office | 2023
|