Published June 21, 2007 | Version 13885
Journal article Open

Object Recognition in Color Images by the Self Configuring System MEMORI

Creators

Description

System MEMORI automatically detects and recognizes rotated and/or rescaled versions of the objects of a database within digital color images with cluttered background. This task is accomplished by means of a region grouping algorithm guided by heuristic rules, whose parameters concern some geometrical properties and the recognition score of the database objects. This paper focuses on the strategies implemented in MEMORI for the estimation of the heuristic rule parameters. This estimation, being automatic, makes the system a self configuring and highly user-friendly tool.

Files

13885.pdf

Files (575.4 kB)

Name Size Download all
md5:b9915118955a31e05f634244249aa974
575.4 kB Preview Download

Additional details

References

  • C. Andreatta. CBIR techniques for object recognition. Technical Report T04-12-01, ITC-irst, Povo, Trento, Italy, December 2004.
  • C. Andreatta, M. Lecca, and S. Messelodi. Memory-based object recognition in images. Technical Report N. T04-12-06, ITC -irst, December 2004
  • C. Andreatta, M. Lecca, and S. Messelodi. Memory-based object recognition in images. In 10th International Fall Workshop - Vision, Modelling, and Visualization - VMV 2005, 2005.
  • R. Brunelli and O. Mich. Image retrieval by examples. IEEE Transactions on Multimedia, 2(3):164-171, 2000.
  • P. Duygulu, K. Barnard, N. de Freitas, and D. Forsyth. Object Recognition as Machine Translation: Learning a lexicon for a fixed image vocabulary. In European Conference on Computer Vision (ECCV) Copenhagen, 2002.
  • D. I. Moldovan, and C.-I. Wu. A Hierarchical Knowledge Based System for Airplane Classification. IEEE Transactions on Software Engineering, 2004, Vol. 14, N. 12, pp. 1828 - 1834
  • O. Carmichael, and M. Hebert. Shape-based Recognition Of Wiry Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, Vol. 26, pp. 1537-1552
  • P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graph-based image segmentation. Int. J. Comput. Vision, 59(2):167-181, 2004.
  • D. A. Forsyth and J. Ponce. Computer Vision: a modern approach. Prentice Hall, 2002. [10] A. Hoogs, R. Collins, R. Kaucic, and J. Mundy. A common set of perceptual observables for grouping, figure - ground discrimination, and texture classification. IEEE Transaction on Pattern Analysis and Machine Intelligence, (4):458-474, 2003. [11] B. Ko and H. Byun. Extracting Salient Regions And Learning Importance Scores In Region-Based Image Retrieval. International Journal of Patter Recognition and Artificial Intelligence, (17(8)):1349-1367, 2003. [12] M. Lecca. MEMORI - version 1.0. Technical Report T05-10-01, ITC - irst, Centro per la Ricerca Scientifica e Tecnologica, October 2005. [13] M. Lecca. A new method for the automatic estimation of the heuristic rule parameters for MEMORI 1.0. Technical Report T05-12-01, ITC - irst, Centro per la Ricerca Scientifica e Tecnologica, December 2005. [14] M. Lecca. A Self Configuring System for Object Recognition in Color Images. Proceedings of 12th International Conference on Computer Science, March 2006 [15] D. I. Moldovan and C.-I. Wu. A hierarchical knowledge based system for airplane classification. IEEE Transactions on Software Engineering, 14(12):1829-1834, 1988. [16] S. A. Nene, S. K. Nayar, and H. Murase. Columbia object image library (COIL-100). In Technical Report CUCS-006-96, Columbia University, 1996. [17] M. Lecca. Test Set GroundTruth100-for-COIL, http://tev.itc.it/DATABASES/objects.html