We propose a texture similarity measure based on the Kullback-Leibler divergence between gamma distributions (KLGamma). We conjecture that the spatially smoothed Gabor filter magnitude responses of some classes of visually homogeneous stochastic textures are gamma distributed. Classification experiments with disjoint test and training images, show that the KLGamma measure performs better than other parametric measures. It approaches, and under some conditions exceeds, the classification performance of the best non-parametric measures based on binned marginal histograms, although it has a computational cost at least an order of magnitude less. Thus, the KLGamma measure is well suited for use in real-time image segmentation algorithms and time-critical texture classification and retrieval from large databases.The authors would like to thank Henrik Schumann-Olsen for his useful comments and discussions. We also thank the anonymous reviewers for their feedback.

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To appropriately utilize the rapidly growing amount of data and information is a big challenge for people and organizations. Standard information retrieval methods, using sequential processing combined with syntax-based indexing and access methods, have not been able to adequately handle this problem. We are currently investigating a different approach, based on a combination of massive parallel processing with case-based (memory-based) reasoning methods. Given the problems of purely syntax-based retrieval methods, we suggest ways of incorporating general domain knowledge into memory-based reasoning. Our approach is related to the properties of the parallel processing microchip MS160, particularly targeted at fast information retrieval from very large data sets. Within this framework different memory-based methods are studied, differing in the type and representation of cases, and in the way that the retrieval methods are supported by explicit general domain knowledge. Cases can be explicitly stored information retrieval episodes, virtually stored abstractions linked to document records, or merely the document records themselves. General domain knowledge can be a multi-relational semantic network, a set of term dependencies and relevances, or compiled into a global similarity metric. This paper presents the general framework, discusses the core issues involved, and describes three different methods illustrated by examples from the domain of medical diagnosis.

Virtualisation of Product Development may be interpreted in two ways: The virtualisation of the “Factory” or the virtualisation of the product development process. In order to virtualise product development, general development environments should be developed. In this talk, I will present some contributions to such environments: TrollCreek – a Case Based Reasoning (CBR) environment for utilization of knowledge and experience. XPLANO a Rule Based system for Automatic Process Planning. TrollEye – an Automatic Visual Inspection (AVI) environment using Machine Vision.

Abstract:Knowledge Management offers enterprises a considerable competitive edge. Together with manufacturing features extracted from CAD models, the knowledge can be used for augmenting several manufacturing processes such as automatic process planning. Xplano is a Feature Recognition and Knowledge Management system using Manufacturing features, company resources and a knowledge base of rules containing "best practice" in the enterprise to automatically generate process plans.