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Fusion of Global Shape and Local Features Using Boosting for Object Class Recognition
- Source :
- International Journal of Computer and Communication Engineering. :444-448
- Publication Year :
- 2012
- Publisher :
- IACSIT Press, 2012.
-
Abstract
- In object class recognition, the state-of-the-art works shows using combination varies local features may produce a good performance in recognition. These local features may have a different performance on one category to other category which it depends on the richness of local features. Due to that limitation, the shape features of objects are taken into consideration to be combined with local features. In this paper, we use Fourier Descriptor (FD), Elliptical Fourier Descriptors (EFD) and Moment Invariant (MI) as a global shape feature and Scale Invariant Feature Transform (SIFT) as local features. For learning technique, boosting is used in improving the recognition objects. This approach identifies the correct and misclassified dataset iteratively. Experimental results indicate that the recognition model outperform improved the accuracy of classification by up to 10% that is comparable to or better than that of state-of-the-art approaches.
- Subjects :
- Fusion
Boosting (machine learning)
Computer science
business.industry
3D single-object recognition
Scale-invariant feature transform
Pattern recognition
symbols.namesake
ComputingMethodologies_PATTERNRECOGNITION
Fourier transform
Computer Science::Computer Vision and Pattern Recognition
Fourier descriptor
symbols
Computer vision
Artificial intelligence
Object class recognition
Invariant (mathematics)
business
Subjects
Details
- ISSN :
- 20103743
- Database :
- OpenAIRE
- Journal :
- International Journal of Computer and Communication Engineering
- Accession number :
- edsair.doi...........692393eff55fa040c7dbc97390822ebf