Case-Based Reasoning on Images and Signals

1. Front Matter

   Pages I-X

   PDF

2. Introduction to Case-Based Reasoning for Signals and Images

   • P. Perner

   Pages 1-24

3. Similarity

   • M. M. Richter

   Pages 25-90

4. Distance Function Learning for Supervised Similarity Assessment

   • A. Bagherjeiran, C. F. Eick

   Pages 91-126

5. Induction of Similarity Measures for Case Based Reasoning Through Separable Data Transformations

   • L. Bobrowski, M. Topczewska

   Pages 127-148

6. Graph Matching

   • X. Jiang, H. Bunke

   Pages 149-173

7. Memory Structures and Organization in Case-Based Reasoning

   • I. Bichindaritz

   Pages 175-194

8. Learning a Statistical Model for Performance Prediction in Case-Based Reasoning

   • B. Bhanu, R. Wang

   Pages 195-212

9. A CBR Agent for Monitoring the Carbon Dioxide Exchange Rate from Satellite Images

   • J. M. Corchado, J. Aiken, J. Bajo

   Pages 213-246

10. Extracting Knowledge from Sensor Signals for Case-Based Reasoning with Longitudinal Time Series Data

    • P. Funk, N. Xiong

    Pages 247-284

11. Prototypes and Case-Based Reasoning for Medical Applications

    • R. Schmidt, T. Waligora, O. Vorobieva

    Pages 285-317

12. Case-Based Reasoning for Image Segmentation by Watershed Transformation

    • M. Frucci, P. Perner, G. Sanniti di Baja

    Pages 319-353

13. Similarity-Based Retrieval for Biomedical Applications

    • L. G. Shapiro, I. Atmosukarto, H. Cho, H. J. Lin, S. Ruiz-Correa, J. Yuen

    Pages 355-387

14. Medical Imagery in Case-Based Reasoning

    • D. C. Wilson, D. O’Sullivan

    Pages 389-418

15. Instance-Based Relevance Feedback in Image Retrieval Using Dissimilarity Spaces

    • G. Giacinto, F. Roli

    Pages 419-436

This book is the ?rst edited book that deals with the special topic of signals and images within case-based reasoning (CBR). Signal-interpreting systems are becoming increasingly popular in medical, industrial, ecological, biotechnological and many other applications. Existing statisticalandknowledge-basedtechniqueslackrobustness,accuracy,and?- ibility. New strategies are needed that can adapt to changing environmental conditions, signal variation, user needs and process requirements. Introducing CBRstrategiesintosignal-interpretingsystemscansatisfytheserequirements. CBR can be used to control the signal-processing process in all phases of a signal-interpreting system to derive information of the highest possible qu- ity. Beyond this CBR o?ers di?erent learning capabilities, for all phases of a signal-interpretingsystem,thatsatisfydi?erentneedsduringthedevelopment process of a signal-interpreting system. In the outline of this book we summarize under the term “signal” signals of 1-dimensional, 2-dimensional or 3-dimensional nature. The unique data and the necessary computation techniques require ext- ordinary case representations, similarity measures and CBR strategies to be utilised. Signalinterpretation(1D,2D,or3Dsignalinterpretation)istheprocessof mapping the numerical representation of a signal into logical representations suitable for signal descriptions. A signal-interpreting system must be able to extract symbolic features from the raw data e.g., the image (e.g., irregular structure inside the nodule, area of calci?cation, and sharp margin). This is a complex process; the signal passes through several general processing steps before the ?nal symbolic description is obtained. The structure of the book is divided into a theoretical part and intoan application-oriented part.

• Case-Based Reasoning
• Signal
• Statistica
• classification
• computational intelligence
• image processing
• image segmentation
• intelligence
• learning
• memory
• model
• multimedia
• performance
• signal processing
• transformation

• Institute of Computer Vision and Applied Computer Sciences, IBaI, Leipzig, Germany

  Petra Perner

Policies and ethics