The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

Classification and regression trees

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Data Mining - Concepts and Techniques.

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 been 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.

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Statistical pattern recognition: a review

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

This paper examines the issue of direct extraction of low level features from compressed images. Specifically, we consider the detection of areas of interest and edges in images compressed using the discrete cosine transform (DCT). For interest areas, we show how a measure based on certain DCT coefficients of a block can provide an indication of underlying activity. For edges, we show using an ideal edge model how the relative values of different DCT coefficients of a block can be used to estimate the strength and orientation of an edge. Our experimental results indicate that coarse edge information from compressed images can be extracted up to 20 times faster than conventional edge detectors.

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Direct feature extraction from compressed images

Machine recognition of constrained hand printed devanagari

The authors propose two automatic threshold-selection schemes, based on functional approximation of the histogram. The first method is based on minimising the sum of square errors, and the second one is based on minimising the variance of the approximated histogram. Experimental results show that, on average, the latter scheme gives better results than the former one, at a small extra computational cost. A 'goodness' measure is proposed to measure the effectiveness of the two schemes, and to compare them against the entropy-based approach and the moment-based approach.

Thresholding based on histogram approximation

The major problem facing video databases is that of content characterization of video clips once the cut boundaries have been determined. The current efforts in this direction are focussed exclusively on the use of pictorial information, thereby neglecting an important supplementary source of content information, i.e. the embedded audio or sound track. The current research in audio processing can be readily applied to create many different video indices for use in Video On Demand (VOD), educational video indexing, sports video characterization, etc. MPEG is an emerging video and audio compression standard with rapidly increasing popularity in multimedia industry. Compressed bit stream processing has gained good recognition among the researchers. We have also demonstrated feature extraction in MPEG compressed video which implements a majority of scene change detection schemes on compressed video. In this paper, we examine the potential of audio information for content characterization by demonstrating the extraction of widely used features in audio processing directly from compressed data stream and their application to video clip classification.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Audio characterization for video indexing

In image similarity retrieval systems, color is one of the most widely used features Users who are not well versed with the image domain characteristics might be more comfortable in working with an Image Retrieval System that allows specification of a query in terms of keywords, thus eliminating the usual intimidation in dealing with very primitive features In this paper we present two approaches to automatic image annotation, by finding those rules underlying the links between the low-level features and the high-level concepts associated with images One scheme uses global color image information and classification tree based techniques Through this supervised learning approach we are able to identify relationships between global color-based image features and some textual decriptors In the second approach, using low-level image features that capture local color information and through a k-means based clustering mechanism, images are organized in clusters such that images that are similar are located in the same cluster For each cluster, a set of rules is derived to capture the association between the localized color-based image features and the textual descriptors relevant to the cluster

Ishwar K. Sethi

Papers

Direct feature extraction from compressed images

Machine recognition of constrained hand printed devanagari

Thresholding based on histogram approximation

Audio characterization for video indexing

Mining association rules between low-level image features and high-level concepts