Why case-based reasoning is attractive for image interpretation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2080, Issue , 2001, Pages 27-43

  Perner, Petra ^a  

^a Institute of Computer Vision and Applied Computer Sciences   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE ANALYSIS;

CASE BASED; CASEBASED REASONINGS (CBR); ENVIRONMENTAL INFLUENCES; IMAGE INTERPRETATION; NEW APPROACHES; RESEARCH TOPICS;

CASE BASED REASONING;

EID: 84956694977     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44593-5_3     Document Type: Conference Paper

References (36)

1. Jarmulak, J. (1998). Case-based classification of ultrasonic B-Scans: Case-base organisation and case retrieval. In B. Smyth & P. Cunningham (Eds.) Advances in Case-Based Reasoning (pp. 100-111). Berlin: Springer Verlag.
2. Grimnes, M. & Aamodt, A.(1996). A two layer case-based reasoning architecture for medical image understanding, In I. Smith & B. Faltings (Eds.) Advances in Case-Based Reasoning (pp. 164-178). Berlin: Springer Verlag.
3. Perner, P. (1999). An architecture for a CBR image segmentation system. Journal of Engineering Application in Artificial Intelligence, 12(6), 749-759.
4. Micarelli, A. Neri, A., & Sansonetti, G. (2000). A case-based approach to image recognition, In E. Blanzieri & L. Portinale (Eds.) Advances in Case-Based Reasoning (pp. 443-454). Berlin: Springer Verlag.
5. Venkataraman, S., Krishnan, R., & Rao, K.K. (1993). A rule-rule-case based system for image analysis. In M.M. Richter, S. Wess, K.D. Althoff, & F. Maurer (Eds.) First European Workshop on Case-Based Reasoning (Technical Report SFB 314) (pp. 410-415). Kaiserslautern, Germany: University of Kaiserslautern.
6. Ficet-Cauchard, V., Porquet, C., & Revenu, M. (1999). CBR for the reuse of image processing knowledge: A recursive retrieval/adaption strategy. In K.-D. Althoff, R. Bergmann, & L.K. Branting (Eds.) Case-Based Reasoning Research and Development (pp. 438-453). Berlin: Springer.
7. Rice, S.V., Nagy, G., & Nartker, T.H. (1999). Optical character recognition: An illustrated guide to the frontier. London: Kluwer.
8. Klette, R., Stiehl, H.S., Viergever, M.A., & Vincken, K.L. (2000). Performance characterization in computer vision. London: Kluwer
9. Perner, P. (1998). Using CBR learning for the low-level and high-level unit of a image interpretation system. In S. Singh (Ed.) Advances in Pattern Recognition (pp. 45-54). Berlin: Springer Verlag.
10. Perner, P. (1999). An architecture for a CBR image segmentation system. In K.-D. Althoff, R. Bergmann, & L. K. Branting (Eds.) Case-Based Reasoning Research and Development (pp. 525-535). Berlin: Springer Verlag.
11. Zamperoni, P. & Starovoitov, V. (1995). How dissimilar are two gray-scale images? In Proceedings of the Seventeenth DAGM Symposium (pp.448-455). Berlin: Springer Verlag.
12. Santini, S. & Jain, R. (1999). Similarity measures IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(9), 871-883.
13. Horikowa, Y. (1996). Pattern recognition with invariance to similarity transformations based on third-order correlations. In Proceedings of Internatinal Confernce on Pattern Recognition '96, IEEE Computer Society Press (pp 200-204).
14. Leitao, F. (1999). A study of string dissimilarity measures in structural clustering. In: S. Singh (Ed.) Advances in Pattern Recognition (pp. 385-394). Berlin: Springer Verlag.
15. Mehrotra, G. (1993). Similar shape retrieval using a structural feature index. Information Systems, 18 (5), 525-537.
16. Cortelazzo, C., Deretta, G., Mian, G.A., & Zamperoni, P. (1996). Normalized weighted Levensthein distance and triangle inequality in the context of similarity discrimination of bilevel images. Pattern Recognition Letters, 17(5), 431-437.
17. Crouzil, A., Massipo-Pail, L., & Castan, S. (1996). A new correlation criterion based on gradient fields similarity. In Proceedings of Internatinal Confernce on Pattern Recognition’96, IEEE Computer Society Press, (pp. 632-636).
18. Moghadda, Nastar, & Pentland (1996). A Bayesian similarity measure for direct image matching. In Proceedings of Internatinal Confernce on Pattern Recognition’96, IEEE Computer Society Press, (pp. 350-358).
19. Moghadda, Jebra, & Pentland (1998). Efficient MAP/ML similarity matching for visual recognition, In Proceedings of Internatinal Confernce on Pattern Recognition '98, IEEE Computer Society Press, (pp. 876-881).
20. Wilson, D.L., Baddely, A.J., & Owens R.A. (1997). A new metric for gray-scale image comparison, International Journal of Computer Vision, 24(1), 5-19.
21. Messmer, B., & Bunke, H. (2000). Efficient subgraph isomorphism detection: A decomposition approach. IEEE Trans. on Knowledge and Data Engineering, 12(2), 307-323.
22. van der Heiden, A., & Vossepoel, A. A landmark-based approach of shape dissimilarity. In Proceedings of the International Conference on Pattern Recognition’99, IEEE Computer Society Press, (pp. 120-124).
23. Perner, P. (1998) Content-based image indexing and retrieval in a image database for technical domains, In H.H.S. Ip & A. Smuelder (Eds.) Multimedia Information Analysis and Retrieval (pp. 207-224). Berlin: Springer Verlag.
24. Voss, A. (Ed.) Similarity Concepts and Retrieval Methods. Fabel Report № 13, GMD-1993, ISSN 0942-413.
25. Perner, P. (1998). Image analysis and classification of HEp-2 cells in fluorescent images, Fourteenth International Conference on Pattern Recognition (pp. 1677-1679). Brisbane Australia: IEEE Computer Society Press.
26. Perner, P., Zscherpel, U., & Jacobsen, C. (2000). Comparison between neural networks and decision trees application of machine learning in industrial radiographic testing. Pattern Recognition Letters, 22/1, 47-54.
27. Perner, P. (1998). Different learning strategies in a case-based reasoning system for image interpretation. In B.Smyth & P. Cunningham (Eds.) Advances in Case-Based Reasoning (pp. 251-261). Berlin: Springer Verlag.
28. Leake, D.B., & Wilson, D.C. (2000). Remembering why to remember: performance-guided case-base maintenance, In E. Blanzieri & L. Portinale (Eds.) Advances in Case-Based Reasoning (pp. 161-172). Berlin: Springer Verlag.
29. Portinale, L., Torasso, P., & Tavano, P. (1999). Speed-up, quality, and competence in multi-modal reasoning In K.-D. Althoff, R. Bergmann, & L.K. Branting (Eds.) Case-Based Reasoning Research and Development (pp. 303-317). Berlin: Springer.
30. Perner, P. (1994), How to use Repertory Grid for Knowledge Acquisition in Image Interpretation. HTWK Leipzig Report Nr. 2.
31. Schmitt, S., Jerusalem, D., & Nett, T. (2000) Representation and execution of questioning strategies for the acquisition of customer requirements, In M.H. Göker (Ed.) Eighth German Workshop on Case-Based Reasoning, p.23-37, DaimlerChrysler Research and Technology FT3/KL.
32. Smyth, B., & McKenna, E. (1998). Modeling the competence of case-bases. In B. Smyth & P. Cunningham (Eds.) Advances in Case-Based Reasoning (pp. 208-220). Berlin: Springer Verlag.
33. Leake, D.B., & Wilson, D.C. (2000). Remembering why to remember: Performance-guided case-base maintenance. In E. Blanzieri & L. Portinale (Eds.) Advances in Case-Based Reasoning (pp. 161-172). Berlin: Springer Verlag.
34. Cordella, Goggia, Sansone, Tortorella, & Vento (1999). Graph matching: A fast algorithm and its evaluation. In Proceedings of the International Conference on Pattern Recognition’99, IEEE Computer Society Press (pp. 1582-1584).
35. Cheetham, W. & Graf, J. (1997), Case-Based Reasoning in Color Matching, In: Leake, D.B. & Plaza, E. (Eds.) Case-Based Reasoning Research and Development, (pp. 1-12).Berlin, Springer Verlag.
36. Bergmann, R. & Wilke, W.(1996), On the Role of Abstraction in Case Based Reasoning, In: Smith, I. & Faltings, B. (Eds.) Advances in Case-Based Reasoning, (pp. 28-43). Berlin, Springer Verlag.