Finding structures in vast multidimensional data sets, be they measurement data, statistics, or textual documents, is difficult and time-consuming. Interesting, novel relations between the data items may be hidden in the data. The selforganizing map (SOM) algorithm of Kohonen can be used to aid the exploration: the structures in the data sets can be illustrated on special map displays. In this work, the methodology of using SOMs for exploratory data analysis or data mining is reviewed and developed further. The properties of the maps are compared with the properties of related methods intended for visualizing highdimensional multivariate data sets. In a set of case studies the SOM algorithm is applied to analyzing electroencephalograms, to illustrating structures of the standard of living in the world, and to organizing full-text document collections. Measures are proposed for evaluating the quality of different types of maps in representing a given data set, and for measuring the robu...

Abstract—Development of content-based image retrieval (CBIR) techniques has suffered from the lack of standardized ways for describing visual image content. Luckily, the MPEG-7, or formally “Moving Pictures Expert Group Multimedia Content Description Interface ” international standard is now emerging as both a general framework for content description and a collection of specific agreed-upon content descriptors. We have developed a neural, self-organizing technique for CBIR. Our system is named PicSOM and it is based on pictorial examples and relevance feedback (RF). The name stems from “picture ” and the self-organizing map (SOM). The PicSOM system is implemented by using tree structured SOMs. In this paper, we apply the visual content descriptors provided by MPEG-7 in the PicSOM system and compare our own image indexing technique with a reference system based on vector quantization (VQ). The results of our experiments show that the MPEG-7-defined content descriptors can be used as such in the PicSOM system even though Euclidean distance calculation, inherently used in the PicSOM system, is not optimal for all of them. Also, the results indicate that the PicSOM technique is a bit slower than the reference system in starting to find relevant images. However, when the strong RF mechanism of PicSOM begins to function, its retrieval precision exceeds that of the reference system. Index Terms—Content-based image retrieval (CBIR), MPEG-7, query by pictorial example (QBPE), relevance feedback (RF), selforganizing map (SOM), visual content description. I.

The recent development of computing hardware has resulted in a rapid increase of visual information such as databases of images. To successfully utilize this increasing amount of data, we need eoeective ways to process it. Content-based image retrieval utilizes the visual content of images directly in the process of retrieving relevant images from a database. The retrieval is based on visual features such as the colors, textures, shapes, and spatial relations the image contains rather than traditional textual keywords. These features are usually extracted automatically, without the need for a human operator. In the literature survey part o...

Digital image libraries are becoming more common and widely used as more visual information is produced at a rapidly growing rate. Content-based image retrieval is an important approach to the problem of processing this increasing amount of data. It is based on automatically extracted features from the content of the images, such as color, texture, shape, and structure. We have started a project to study methods for content-based image retrieval using the Self-Organizing Map (SOM) as the image similarity scoring method. Our image retrieval system, named PicSOM, can be seen as a SOM-based approach to relevance feedback which is a form of supervised learning to adjust the subsequent queries based on the user's responses during the information retrieval session. In PicSOM, a separate Tree Structured SOM (TS-SOM) is trained for each feature vector type in use. The system then adapts to the user's preferences by returning her more images from those SOMs where her responses have been most de...

Kohonen's Self-Organizing Map (SOM) is one of the most popular artificial neural network algorithms. Word category maps are SOMs that have been organized according to word similarities, measured by the similarity of the short contexts of the words. Conceptually interrelated words tend to fall into the same or neighboring map nodes. Nodes may thus be viewed as word categories. Although no a priori information about classes is given, during the self-organizing process a model of the word classes emerges. The central topic of the thesis is the use of the SOM in natural language processing. The approach based on the word category maps is compared with the methods that are widely used in artificial intelligence research. Modeling gradience, conceptual change, and subjectivity of natural language interpretation are considered. The main application area is information retrieval and textual data mining for which a specific SOM-based method called the WEBSOM has been developed. The WEBSOM metho...

The Self-Organizing Map is a very popular unsupervised neural network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are related, on the one hand, to the static architecture of this model, as well as, on the other hand, to the limited capabilities for the representation of hierarchical relations of the data. With our novel Growing Hierarchical SelfOrganizing Map presented in this paper we address both limitations. The Growing Hierarchical Self-Organizing Map is an arti cial neural network model with hierarchical architecture composed of independent growing self-organizing maps. The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data. Furthermore, by providing a global orientation of the independently growing maps in the individual layers of the hierarchy, navigation across branches is facilitated. The bene ts of this novel neural network are rst, a problem-dependent architecture, and second, the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion.

Abstract. As digital music collections grow, so does the need to organizing them automatically. In this paper we present an approach to hierarchically organize music collections at the artist level. Artists are grouped according to similarity which is computed using a web search engine and standard text retrieval techniques. The groups are described by words found on the webpages using term selection techniques and domain knowledge. We compare different term selection techniques, present a simple demonstration, and discuss our findings. 1

This work presents experiments to recognize pattern sequences using hidden Markov models (HMMs). The pattern sequences in the experiments are computed from speech signals and the recognition task is to decode the corresponding phoneme sequences. The training of the HMMs of the phonemes using the collected speech samples is a difficult task because of the natural variation in the speech. Two neural computing paradigms, the Self-Organizing Map (SOM) and the Learning Vector Quantization (LVQ) are used in the experiments to improve the recognition performance of the models. A HMM consists of sequential states which are trained to model the feature changes in the signal produced during the modeled process. The output densities applied in this work are mixtures of Gaussian density functions. SOMs are applied to initialize and train the mixtures to give a smooth and faithful presentation of the feature vector space defined by the corresponding training samples. The SOM maps similar feature vect...

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The aim of the thesis was to study the use of shape features in content-based image retrieval and to implement shape features to such a system called PicSOM. Because of the internal structure and principles of PicSOM the emphasis was put on such shape feature techniques which can be represented by constant-sized feature vectors and with which the Euclidean distance can be used as a similarity measure. The review covers the best-known shape description techniques which have been published in scientic journals and conference proceedings. Based on the review, some techniques were selected for the...