Boosted complex moments for discriminant rotation invariant object recognition

This paper proposes a method for constructing a discriminative rotation invariant object recognition system from the set of complex moments by using a multi-class boosting algorithm. Experimental results show that a large of number images can be discriminated accurately with only a small number of features. This basically means economy of computational effort in feature acquisition and also possibility of higher speed in recognition task.

[1]  Raveendran Paramesran,et al.  Image analysis by Krawtchouk moments , 2003, IEEE Trans. Image Process..

[2]  Demetri Psaltis,et al.  Recognitive Aspects of Moment Invariants , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  M. Teague Image analysis via the general theory of moments , 1980 .

[4]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[5]  Sim Heng Ong,et al.  Image Analysis by Tchebichef Moments , 2001, IEEE Trans. Image Process..

[6]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[7]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[8]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[9]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Raveendran Paramesran,et al.  Image Analysis Using Hahn Moments , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Demetri Psaltis,et al.  Image Normalization by Complex Moments , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.