Image primitives

Towards the construction of a content based image recognition system it is important to isolate and also represent image segments. There have been several proposed methods of representing a shape but many are not sensitive to deviations of the appearance of an object. Furthermore, it is noted that the set of possible shapes is not distributed equally within a representation space. Thus, liberating the requirement that a basis set be orthonormal is justified. A method is presented here that extracts a basis set from an image database and defines shapes from this basis set. The results indicate that describing shapes from this basis set is robust to alterations of the shape such as small occlusions, limited skew, and limited range.

[1]  Thomas S. Huang,et al.  Supporting similarity queries in MARS , 1997, MULTIMEDIA '97.

[2]  James D. Brasher,et al.  Fractional-power synthetic discriminant functions , 1994, Pattern Recognit..

[3]  Jason M. Kinser.  Image primitive signatures , 2004, 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04).

[4]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Tapas Kanungo,et al.  Object recognition using appearance-based parts and relations , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Jason M. Kinser,et al.  Image Processing using Pulse-Coupled Neural Networks , 1998, Perspectives in Neural Computing.

[7]  Alan L. Yuille,et al.  FORMS: A flexible object recognition and modelling system , 1996, International Journal of Computer Vision.

[8]  H. S. Ranganath,et al.  Image segmentation using pulse coupled neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[9]  Jeffery Johnson,et al.  Pulse coupled neural networks for image processing , 1995, Proceedings IEEE Southeastcon '95. Visualize the Future.

[10]  N. Karayiannis.  MECA: maximum entropy clustering algorithm , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[11]  Lakshman Prasad,et al.  Neural network for image segmentation , 2000, SPIE Optics + Photonics.