Introduction to the theory of neural computation
Introduction to the theory of neural computation
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Review article: Local adaptive receptive field self-organizing map for image color segmentation
Image and Vision Computing
Novel fast color reduction algorithm for time-constrained applications
Journal of Visual Communication and Image Representation
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
New adaptive color quantization method based on self-organizing maps
IEEE Transactions on Neural Networks
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A new approach for image compression based on the GHSOM model has been proposed in this paper. The SOM has some problems related to its fixed topology and its lack of representation of hierarchical relations among input data. The GHSOM solves these limitations by generating a hierarchical architecture that is automatically determined according to the input data and reflects the inherent hierarchical relations among them. These advantages can be utilized to perform a compression of an image, where the size of the codebook (leaf neurons in the hierarchy) is automatically established. Moreover, this hierarchy provides a different compression at each layer, where the deeper the layer, the lower the rate compression and the higher the quality of the compressed image. Thus, different trade-offs between compression rate and quality are given by the architecture. Also, the size of the codebooks and the depth of the hierarchy can be controlled by two parameters. Experimental results confirm the performance of this approach.