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.

Decision trees have proved to be valuable tools for the description, classification and generalization of data. Work on constructing decision trees from data exists in multiple disciplines such as statistics, pattern recognition, decision theory, signal processing, machine learning and artificial neural networks. Researchers in these disciplines, sometimes working on quite different problems, identified similar issues and heuristics for decision tree construction. This paper surveys existing work on decision tree construction, attempting to identify the important issues involved, directions the work has taken and the current state of the art. Keywords: classification, tree-structured classifiers, data compaction 1. Introduction Advances in data collection methods, storage and processing technology are providing a unique challenge and opportunity for automated data exploration techniques. Enormous amounts of data are being collected daily from major scientific projects e.g., Human Genome...

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In recent years, multiscale Bayesian approaches have attracted increasing attention for use in image segmentation. Generally, these methods tend to offer improved segmentation accuracy with reduced computational burden. Existing Bayesian segmentation methods use simple models of context designed to encourage large uniformly classified regions. Consequently, these context models have a limited ability to capture the complex contextual dependencies that are important in applications such as document segmentation. In this paper, we propose a multiscale...

This paper presents automatic methods for the segmentation and classification of dialog acts (DA). In Verbmobil it is often sufficient to recognize the sequence of DAs occurring during a dialog between the two partners. Since a turn can consist of one or more successive DAs we conduct the classification of DAs in a two step procedure: First each turn has to be segmented into units which correspond to a DA and second the DA categories have to be identified. For the segmentation we use polygrams and multi-layer perceptrons, using prosodic features. The classification of DAs is done with semantic classification trees and polygrams.

Pruning a decision tree is considered by some researchers to be the most important part of tree building in noisy domains. While, there are many approaches to pruning, an alternative approach of averaging over decision trees has not received as much attention. We perform an empirical comparison of pruning with the approach of averaging over decision trees. For this comparison we use a computationally efficient method of averaging, namely averaging over the extended fanned set of a tree. Since there are a wide range of approaches to pruning, we compare tree averaging with a traditional pruning approach, along with an optimal pruning approach.

Induced decision trees are an extensively-researched solution to classification tasks. For many practical tasks, the trees produced by tree-generation algorithms are not comprehensible to users due to their size and complexity. Although many tree induction algorithms have been shown to produce simpler, more comprehensible trees (or data structures derived from trees) with good classification accuracy, tree simplification has usually been of secondary concern relative to accuracy and no attempt has been made to survey the literature from the perspective of simplification. We present a framework that organizes the approaches to tree simplification and summarize and critique the approaches within this framework. The purpose of this survey is to provide researchers and practitioners with a concise overview of tree-simplification approaches and insight into their relative capabilities. In our final discussion, we briefly describe some empirical findings and discuss the application of tree i...

In this paper, we present an approach to optimal image scaling called Tree-Based Resolution synthesis (TBRS). TBRS works by first performing a fast local classification of a window around the pixel being interpolated, and then by applying an interpolation filter designed for the selected class. The idea behind TBRS is to use a regression tree as a piecewise linear approximation to the conditional mean estimator of the high-resolution image given the lowresolution image. We generate the parameters for the regression tree by training on sample images. The training is computationally demanding, but it only needs to be performed once. We will demonstrate that the resulting predictor may be used effectively on input images that were not used in the training. 1 Introduction An image is usually only available at one or a few resolutions, so that in order to render it at a higher resolution, some image scaling technique must be applied. This involves some inference on the part of the interpo...

In this paper, we describe a unique new paradigm for video database management known as ViBE (Video Indexing and Browsing Environment). ViBE is a browseable/searchable paradigm for organizing video data containing a large number of sequences. We describe how ViBE performs on a database of MPEG sequences.

We introduce an adaptive wavelet graph image model applicable to Bayesian tomographic reconstruction and other problems with non-local observations. The proposed model captures coarse-to-fine scale dependencies in the wavelet tree by modeling the conditional distribution of wavelet coefficients given overlapping windows of scaling coefficients containing coarse scale information. This results in a graph dependency structure which is more general than a quadtree, enabling the model to produce smooth estimates even for simple wavelet bases such as the Haar basis. The inter-scale dependencies of the wavelet graph model are specified using a spatially non-homogeneous Gaussian distribution with parameters at each scale and location. The parameters of this distribution are selected adaptively using nonlinear classification of coarse scale data. The nonlinear adaptation mechanism is based on a set of training images. In conjunction with the wavelet graph model, we present a computationally efficient multiresolution image reconstruction algorithm. This algorithm is based on iterative Bayesian space domain optimization using scale recursive updates of the wavelet graph prior model. In comparison to performing the optimization over the wavelet coefficients, the space domain formulation facilitates enforcement of pixel positivity constraints. Results indicate that the proposed framework can improve reconstruction quality over fixed