CBR for the management and reuse of image-processing expertise: a conversational system

Abstract

International audienceThe development of an image-processing (IP) application is a complex activity, which can be greatly alleviated by user-friendly graphical programming environments. The major objective of the work described in this paper is to help IP experts reuse parts of their applications. A first step towards knowledge reuse has been to propose a suitable representation of the strategies of IP experts by means of IP plans (trees of tasks, methods and tools). This paper describes the CBR module of an interactive system for the development of IP plans. After a brief presentation of the overall architecture of the system and its other modules, the authors explain the distinction between an IP case and an IP plan, and give the selection criteria and functions that are used for similarity calculation. The core of the CBR module is a search/adaptation algorithm, whose main steps are detailed: retrieval of suitable cases, recursive adaptation of the selected one and memorization of new cases. The system's implementation is presently completed; its functioning is described in a session showing the kind of assistance provided by the CBR module during the development of a new IP application

An Ontology-Based Model for Representing Image Processing Application Objectives

“…This new version of the software generator will be based on the case-based reasoning paradigm. This paradigm has already been used in image processing [36,18] and in particular by our team [17]. A case is an image processing solution in terms of a chain of algorithms.…”

Case Based Reasoning Support for Adaptive Finite Element Analysis: Mesh Selection for an Integrated System

“…To overcome this issue, which plays a vital role in acquisition of accurate solution through limited number of iterations, application of an advanced AI paradigm CBR is being proposed. CBR promises a technique to support modeling by reusing previous experiences in supporting the new situations (Ficet-Cauchard, Porquet, & Revenu, 1999;Praehofer & Kerschbaummayr, 1999;Surma & Braunschweig, 1996).…”

Formalising optimal feature weight setting in case based diagnosis as linear programming problems

“…The underlying assumption for CBR is that similar cases will have similar solutions. Many CBR approaches have been reported for solving problems in various domains, such as planning [11], design [14], software engineering [4,42], image processing [21,40], and diagnosis [8,31,49].…”