Abstracting visual percepts to learn concepts

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

Volumn 2371, Issue , 2002, Pages 256-273

  Zucker, Jean Daniel ^a   Bredeche, Nicolas ^a,b   Saitta, Lorenza ^c  

^a UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

^b CNRS   (France)

^c UNIVERSITY OF EASTERN PIEDMONT   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS; LEARNING ALGORITHMS; MOBILE ROBOTS;

AUTONOMOUS MOBILE ROBOT; ESSENTIAL ABILITIES; FILTER MODEL; IMAGE DESCRIPTIONS; LEARNING TASKS; PERCEPTUAL REPRESENTATIONS; PERCEPTUAL SYSTEM; WRAPPER MODEL;

ABSTRACTING;

EID: 84956856389     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45622-8_19     Document Type: Conference Paper

References (26)

1. Coradeschi, S., Saffiotti, A.: Anchoring symbols to sensor data: preliminary report. In: Proceedings of AAAI-2000, Austin, Texas (July 2000)
2. Steels, L.: The talking heads experiment. volume 1. words and meanings. In: Antwerpen. (1999)
3. Thrun, S., Bennewitz, M., Burgard, W., Cremers, A., Dellaert, F., Fox, D., H hnel, D., Rosenberg, C., Roy, N., Schulte, J., Schulz, D.: Minerva: A second generation mobile tour-guide robot. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). (1999)
4. Stone, J.: Computer vision: What is the object? In: Prospects for AI, Proc. Artificial Intelligence and Simulation of Behaviour. Birmingham, England., IOS Press, Amsterdam. pages 199–208 (1993)
5. Klingspor, V., Morik, K., Rieger, A.D.: Learning concepts from sensor data of a mobile robot. Machine Learning 23 (1996) 305–332
6. Saitta, L., Zucker, J.D.: A model of abstraction in visual perception. In: Applied Artificial Intelligence. 15(8): 761-776. (2001)
7. Picault, S., Drogoul, A.: The microbes project, an experimental approach towards open collective robotics. In: Proc. of the 5th International Symposium on Distributed Autonomous Robotic Systems, Springer-Verlag Tokyo Inc. (2000)
8. Giordana, A., Saitta, L.: Phase transitions in relational learning. Machine Learning Journal 41 (2000) 217–
9. Stricker, M., Swain, M.: The capacity and the sensitivity of color histogram indexing. In: Technical Report 94-05, Communications Technology Lab, ETH-Zentrum. (1994)
10. Saitta, L., Zucker, J.D.: Semantic abstraction for concept representation and learning. In AAAI), S.i.P.b., ed.: Symposium on Abstraction, Reformulation and Approximation (SARA98), Asilomar Conference Center, Pacific Grove, California (1998)
11. Drury, S.: A Guide to Remote Sensing. The Kluwer International Series on Information Retrieval, 11, Oxford (1990)
12. Saitta, L., Zucker, J.D.: A model of abstraction in visual perception. Applied Artificial Intelligence 15 (2001) 761–776
13. Giordana, A., Saitta, L.: Abstraction: a general framework for learning. In: Working notes of the AAAI Workshop on Automated Generation of Approximations and Abstraction, Boston, MA (1990) 245–256
14. Sacerdoti, E.: Planning in a hierarchy of abstraction spaces. Artificial Intelligence 5 (1974) 115–135
15. Chevaleyre, Y., Zucker, J.D.: A framework for learning rules from multiple instance data. In: Proc. European Conference on Machine Learning (ECML2001). (ECML2001)
16. Steels, L.: The origin of syntax in visually grounded robotic agents. In: Proceedings of IJCAI97, Morgan Kaufman Pub. Los Angeles. (1997)
17. Beymer, D., Konolige, K.: Real-time tracking of multiple people using continous detection. In: Proceedings of the International Conference on Computer Vision (ICCV’99). (1999)
18. Wang, J.Z.: Integrated Region-based Image Retrieval. The Kluwer International Series on Information Retrieval, 11, Oxford (2001)
19. Hsieh, I., Fan, K.: Color image retrieval using shape and spatial properties. In: ICPR00, Vol.I: pp 1023-1026. (2000)
20. Brooks, R.: Intelligence without representation. Artificial Intelligence 47 (1991) 139–159
21. Marr, D.: Vision. Freeman and Co., Oxford (1982)
22. Goldstone, R.L.: Perceptual learning. In: Annual Reviews of Psychology. 49:585-612. (1998)
23. Kohavi, R., John, G.: The wrapper approach. In: Feature Selection for Knowledge Discovery and Data Mining, H. Liu and H. Motoda (eds.), Kluwer Academic Publishers, pp 33-50. (1998)
24. Kohavi, R., Sommerfield, D.: Feature subset selection using the wrapper method: Overfitting and dynamic search space In: International Conference on Knowledge Discovery and Data Mining. (1995)
25. Giunchiglia, F.: Using abstrips abstractions where do we stand ? Artificial Intelligence Review 13 (1996) 201–213
26. Rehrmann, V., Priese, L.: Fast and robust segmentation of natural color scenes. In: Asian Conference on Computer Vision, Hongkong, China. (1998)