Probabilistic algorithms are presented for efficient storage and retrieval of sets of feature vectors, given a known error process operating on the query set, that perturbs the query set away from the corresponding stored set. The algorithms operate by mapping each set to a corresponding generalized indicator vector and then performing a pruned search of a tree containing stored indicator vectors. The pruning is based on the probability of the query, given the stored items below the current position in the tree. Analysis and trial results show that this approach requires less total computation than existing methods based on parallel architectures. The indicator vector retrieval method can also cope efficiently with query vectors of much higher dimensionality than existing serial algorithms for nearest-neighbor searches.<>


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Efficient serial associative memory


    Contributors:
    Wilkes, D. (author) / Tsotsos, J.K. (author)


    Publication date :

    1993-01-01


    Size :

    191102 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Associative Memory Network Construction Algorithms

    Brown, N. / Harris, C. J. / Bridgett, N. A. et al. | British Library Conference Proceedings | 1994


    A compact optical associative memory

    Burns, T.J. / Rogers, S.K. / Kabrisky, M. et al. | Tema Archive | 1991


    Complex-valued multidirectional associative memory

    Kobayashi, Masaki | Online Contents | 2007


    Scaling behavior in holographic associative memory

    Guerrero, R.A. / Arangcon, R.B. | IEEE | 2003