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.


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    Title :

    Image-content classification using a dynamically allocated ALISA texture module


    Contributors:
    Ko, T. (author) / Bock, P. (author)


    Publication date :

    2000-01-01


    Size :

    1207518 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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