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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Catalbas, M.C. Ozkazanc, Y. |
| Copyright Year | 2014 |
| Description | Author affiliation: Elektrik ve Elektron. Muhendisligi Bolumu, Hacettepe Univ., Ankara, Turkey (Catalbas, M.C.; Ozkazanc, Y.) |
| Abstract | This work investigates the role of canonical correlations analysis in image classification problems. Canonical correlation analysis is proposed as an alternative feature selection and reduction method for generic image classification problems. This new method is studied via various image classification problems in comparison with principal components and kernel principal components analysis. Multiple canonical correlation analysis is proposed as a new feature selection and dimension reduction algorithm for image classification problems involving multiple classes. Classification performance and relationship between the extracted image attributes and classification performance are studied by using Caltech 101 dataset. |
| Starting Page | 1011 |
| Ending Page | 1014 |
| File Size | 931389 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781479948741 |
| DOI | 10.1109/SIU.2014.6830403 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-04-23 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Correlation Conferences image classification feature extraction Abstracts multi linear discriminant analysis Signal processing Feature extraction Kernel Canonical correlation analysis multi canonical correlation analysis Image classification |
| Content Type | Text |
| Resource Type | Article |
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