Object Recognition in Color Images by the Self Configuring System MEMORI

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.

Authors:

• Michela Lecca

Keywords:

• Automatic Object Recognition
• Clustering
• Contentbased Image Retrieval System
• Image Segmentation
• Region Adjacency Graph
• Region Grouping.

References:

[1] C. Andreatta. CBIR techniques for object recognition. Technical Report
T04-12-01, ITC-irst, Povo, Trento, Italy, December 2004.
[2] C. Andreatta, M. Lecca, and S. Messelodi. Memory-based object
recognition in images. Technical Report N. T04-12-06, ITC -irst,
December 2004
[3] 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.
[4] R. Brunelli and O. Mich. Image retrieval by examples. IEEE Transactions
on Multimedia, 2(3):164-171, 2000.
[5] 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.
[6] 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
[7] 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
[8] P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graph-based image
segmentation. Int. J. Comput. Vision, 59(2):167-181, 2004.
[9] 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