The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount of interest among researches and practitioners in a wide variety of elds The SOM and a variant of it, the LVQ, have been analyzed extensively, a number of variants of them have been developed and, perhaps most notably, they have been applied extensively within elds ranging from engineering sciences to medicine, biology, and economics We have collected a comprehensive list of 3343 scienti c papers that use the algorithms, have bene ted from them, or contain analyses of them The list is intended to serve as a source for literature surveys We have provided both a thematic and a keyword index to help nding articles of interest

Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997

We present a modification of Kohonens SOFMin order to simulate cortical plasticity induced by coactivation patterns. This is accomplished by introducing a probabilistic mode of stimulus presentation and by substituting the winner-takes- all mechanism by selecting the winner from a set of best matching neurons.

Modification of Kohonen's SOFM to Simulate Cortical Plasticity Induced by Coactivation Input Patterns

The time adaptive self-organizing map (TASOM) network is a modified self-organizing map (SOM) network with adaptive learning rates and neighborhood sizes as its learning parameters. Every neuron in the TASOM has its own learning rate and neighborhood size. For each new input vector, the neighborhood size and learning rate of the winning neuron and the learning rates of its neighboring neurons are updated. A scaling vector is also employed in the TASOM algorithm for compensation against scaling transformations. Analysis of the updating rules of the algorithm reveals that the learning parameters may increase or decrease for adaptation to a changing environment, such that the minimum increase or decrease is achieved according to a specific measure. Several versions of the TASOM-based networks are proposed in this paper for different applications, including bilevel thresholding of grey level images, tracking of moving objects and their boundaries, and adaptive clustering. Simulation results show satisfactory performance of the proposed methods in the implemented applications.

TASOM: a new time adaptive self-organizing map

Several methods have been proposed to do color quantization for limited color displays. Self-organization of Kohonen feature maps (SOFM) is a very useful tool for data clustering. By extracting a special butterfly-jumping sequence from an image, a neural network was fed in that sequence of image data for SOFM training, and a fairly good color table was present to represent that image. The peak signal-to-noise ratio of the decoded image is high (about 35 dB in average for a 256 color image), and the perceptual quality is good as well, even with a small set of color tables (for example, 32 or 64 colors). Furthermore, the training process is fast. For encoding, we also propose an efficient algorithm based on the Kohonen map ordering property. By considering that the neighboring image pixels are closely related and by setting some acceptable thresholds, we can quickly get an encoded image. For further compression on the color indexed image with the limited color palette, we cut the indexed images into 4/spl times/4 blocks and send the block vectors into another SOFM neural network for training. Under the two-dimensional (2-D) mesh neural structure, SOFM vector quantization on an indexed image could largely reduce the color shift artifacts and avoid the requantization problem. About 0.5 b per pixel of coded image can be easily obtained with a fairly good perceptual quality. More importantly, the decoded color indexed images can be readily displayed. This will reduce the decoder complexity greatly.

Color image compression and limited display using self-organization Kohonen map

The time-decreasing learning rate and neighborhood function of the basic SOM (self-organizing map) algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changing environments, we propose a modified SOM algorithm called "time adaptive SOM", or TASOM, that automatically adjusts the learning rate and neighborhood size of each neuron independently. The proposed TASOM is tested with stationary environments and its performance is compared with that of the basic SOM. It is also tested with non-stationary environments for representing the letter 'L', which may be translated, rotated, or scaled. Moreover, the TASOM is used for adaptive segmentation of images which may have undergone gray-level transformation.

TASOM: the time adaptive self-organizing map

In this paper, we propose a binary-tree structure neural network model suitable for structured clustering. During and after training, the centroids of the clusters in this model always form a binary tree in the input pattern space. This model is used to design tree search vector quantization codebooks for image coding. Simulation results show that the acquired codebook not only produces better-quality images but also achieves a higher compression ratio than conventional tree search vector quantization. When source coding is applied after VQ, the new model performs better than the generalized Lloyd algorithm in terms of distortion, bits per pixel, and encoding complexity for low-detail and medium-detail images. >

Tser-Tzi Tang

Papers

Vector quantization using tree-structured self-organizing feature maps