This paper presents a compression scheme for color images, by using Self-Organizing Feature Map algorithm which is a neural network structure. In this application 1-dimensional SOFM is used to map 256-color to 64-, 32and 16-color. After the quantization process, relative coding and entropy coding are performed without any loss in the information.

Kohonen’s se l f -organizing feature map belongs to a class of unsupervised artificial neural network commonly referred to as topographic maps. It serves two purposes , the quantization and dinlensionality reduct ion of data . A short descr ipt ion of i t s h is tory and i ts b io logical context Ls g iven. We show that the inherent c lass i f icat ion propcrtim of the feature map make it a sui...

Invielen Föllen entspricht die Verteilung von empirisch erhobenen Daten nicht einer Normalverteilung. Um eine vergleichbare Slcalierung der Daten zu effeichen, ist eine Transformation in eine Normalverteilung oder zumindest in eine symmetrische Verteilung notwendig. Desweiteren basieren viele statistische verfahren auf der Annahme einer Normalverteilung. Die Bestimmung einer geeigneten Transfor...

The magniication exponents occuring in adaptive map formation algorithms like Kohonen's self-organizing feature map deviate for the information theoretically optimal value = 1 as well as from the values which optimize, e.g., the mean square distortion error (= 1=3 for one-dimensional maps). At the same time, models for categorical perception such as the \perceptual magnet" eeect which are based...

Obtaining semantic or functional word categories from data in an unsupervised manner is a problem motivated both from the linguistic point of view and from that of construing language models for various language processing tasks. In this work, we use the self-organizing map algorithm to visualize and cluster common Finnish verbs based on functional and semantic information coded by case marking...

Introduction Geographic data are often very large in volume and “characterized by a high number of attributes or dimensions” [1]. There are urgent needs to develop effective and yet efficient approaches for analyzing such voluminous and high-dimensional data to address complex geographic problems [1, 2, 3, 4], e.g., detecting unknown multivariate patterns or relationships between socioeconomic,...

Self-organizing mapping is an unsupervised learning paradigm used in pattern classification and hence artificial intelligence. This paradigm is based on modifying the class features via the incoming input stimuli. Its exciting part is that it introduces concepts such as neighborhood or mapping. Hence the results obtained from this paradigm highly depend on the selected neighborhood and mapping ...

Automatic detection and correction of image orientation is of great importance in intelligent image processing. In this paper, we present an automatic image orientation detection algorithm based on the supervised selforganizing map (SOM). The SOM is trained by using compact and efficient low-level chrominance (color) features in a supervised manner. Experiments have been conducted on a database...

-Classification, a data mining task is an effective method to classify the data in the process of Knowledge Data Discovery. Classification method algorithms are widely used in medical field to classify the medical data for diagnosis. Feature Selection increases the accuracy of the Classifier because it eliminates irrelevant attributes. This paper analyzes the performance of neural network class...

We present two enhanced visualization techniques for the self-organizing map allowing the intuitive representation of input data similarity. The general idea of both approaches is to visualize the relationship of nodes to facilitate the detection of cluster boundaries without modifying the architecture or the basic training process of SOM. One approach mirrors the movement of weight vectors dur...