In this paper, the authors came up with a different approach based on the combination of the different descriptors. For object recognition, regardless of orientation, size and position, feature vectors are computed with the help of Zernike moments and Centrist descriptors. For a large data base the fact of using the classic descriptors has never been a satisfying method for perfect recognition rates. The authors deduced that the combination of descriptors can have good recognition rates, accordingthe result of a comparative study of the different descriptors and the different combinations (Zernike + Centrist, Zernike + ACP, Centrist + ACP). The Zernike moment with Centrist descriptors ended up being the best hybrid description. For the recognition process, the authors opted for support vector machine (SVM) and Neural Networks (NN). The authors illustrate the proposed method on 3D objects using representations of two-dimensional images that are taken from different angles of view are the main features leading the authors to their objective.
A comparative study of support vector machine and neural networks for file type identification using n-gram analysis
