The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have bean receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

This report deals with efficient retrieval of images from large databases based on the color and shape content in images. With the increasing popularity of the use of large volume image databases in various applications, it becomes imperative to build an automatic and efficient retrieval system to browse through the entire database. Techniques using textual attributes for annotations are limited in their applications. Our approach relies on image features that exploit visual cues such as color and shape. Unlike previous approaches which concentrate on extracting a single concise feature, our technique combines features that represent both the color and shape in images. Experimental results on a database of 400 trademark images show that an integrated color- and shape-based feature representation results in 99% of the images being retrieved within the top two positions. Additional results demonstrate that a combination of clustering and a branch and bound-based matching scheme aids in i...

Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section.

We propose a rudimentary taxonomy of interactive data visualization based on a triad of data analytic tasks: finding Gestalt, posing queries, and making comparisons. These tasks are supported by three classes of nteractive view manipulation: focusing, linking and arranging views. This discussion extends earlier work on the principles of focusing and linking and sets them on a firmer base. Next, we give a high-level introduction to a particular system for multivariate data visualization: XGobi. This introduction is not comprehensive but emphasizes XGobi tools that are examples of focusing, linking and arranging views, namely: high-dimensional projections, linked scatterplot brusing, and matrices of conditional plots.

We present a new visualization method for 2d flows which allows us to combine multiple data values in an image for simultaneous viewing. We utilize concepts from oil painting, art, and design as introduced in [1] to examine problems within fluid mechanics. We use a combination of discrete and continuous visual elements arranged in multiple layers to visually represent the data. The representations are inspired by the brush strokes artists apply in layers to create an oil painting. We display commonly visualized quantities such as velocity and vorticity together with three additional mathematically derived quantities: the rate of strain tensor (defined in section 4), and the turbulent charge and turbulent current (defined in section 5). We describe the motivation for simultaneously examining these quantities and use the motivation to guide our choice of visual representation for each particular quantity. We present visualizations of three flow examples and observations concerning some o...

This paper presents a new method for using texture and color to visualize multivariate data elements arranged on an underlying height field. We combine simple texture patterns with perceptually uniform colors to increase the number of attribute values we can display simultaneously. Our technique builds multicolored perceptual texture elements (or pexels) to represent each data element. Attribute values encoded in an element are used to vary the appearance of its pexel. Texture and color patterns that form when the pexels are displayed can be used to rapidly and accurately explore the dataset. Our pexels are built by varying three separate texture dimensions: height, density, and regularity. Results from computer graphics, computer vision, and human visual psychophysics have identified these dimensions as important for the formation of perceptual texture patterns. The pexels are colored using a selection technique that controls color distance, linear separation, and color category. Prop...

Visual data mining techniques have proven to be of high value in exploratory data analysis and they also have a high potential for mining large databases. In this article, we describe and evaluate a new visualization-based approach to mining large databases. The basic idea of our visual data mining techniques is to represent as many data items as possible on the screen at the same time by mapping each data value to a pixel of the screen and arranging the pixels adequately. The major goal of this article is to evaluate our visual data mining techniques and to compare them to other well-known visualization techniques for multidimensional data: the parallel coordinate and stick figure visualization techniques. For the evaluation of visual data mining techniques, in the first place the perception of properties of the data counts, and only in the second place the CPU time and the number of secondary storage accesses are important. In addition to testing the visualization techniques using re...

We present a survey of multidimensional multivariate (mdmv) visualization techniques developed during the last three decades. This subfield of scientific visualization deals with the analysis of data with multiple parameters or factors, and the key relationships among them. The course of development is roughly organized into four stages, within which major milestones are discussed. Recently developed techniques are explored with examples. 1 Introduction Multidimensional multivariate visualization is an important subfield of scientific visualization. It was studied separately by statisticians and psychologists long before computer science was deemed a discipline. The appearance of low-priced personal computers and workstations during the 1980's breathed new life into graphical analysis of mdmv data. This research topic was among one of the short-term goals included in the 1987 National Science Foundation (NSF) sponsored workshop on Visualization in Scientific Computing [MDB87]. Th...

Geometric measurements of the human hand have been used for identity authentication in a number of commercial systems. In this project we have developed a protoype hand geometry-based verification system and analyzed it's performance. We have demonstrated the practical utility of this system by designing an application that uses hand geometry as opposed to password for restricting access to a web site. We present our preliminary verification results based on hand measurements of 50 individuals captured over a period of time.

Traditional visualization techniques for multidimensional data sets, such as parallel coordinates, glyphs, and scatterplot matrices, do not scale well to high numbers of dimensions. A common approach to solving this problem is dimensionality reduction. Existing dimensionality reduction techniques usually generate lower dimensional spaces that have little intuitive meaning to users and allow little user interaction. In this paper we propose a new approach to handling high dimensional data, named Visual Hierarchical Dimension Reduction (VHDR), that addresses these drawbacks. VHDR not only generates lower dimensional spaces that are meaningful to users, but also allows user interactions in most steps of the process. In VHDR, dimensions are grouped into a hierarchy, and lower dimensional spaces are constructed using clusters of the hierarchy. We have implemented the VHDR approach into XmdvTool, and extended several traditional multidimensional visualization methods to convey dimension cluster characteristics when visualizing the data set in lower dimensional spaces. Our case study of applying VHDR to a real data set supports our belief that this approach is effective in supporting the exploration of high dimensional data sets.