Design of a LVQ neural network for compressed image indexing

This paper proposes an LVQ neural network design to retrieve compressed images from visual databases by content based technology. For image compression, a so called distortion equalized fuzzy learning algorithm is proposed to vector quantize all images before they are stored in the database. For image indexing, a weighted counting of codeword scheme is designed to construct histograms to address the visual database. Experiments show that improved performance has been achieved with the proposed network for both image compression and indexing.