Abstract: The explosive growth of image data leads to the research and development of image content searching and indexing systems. Image annotation systems aim at annotating automatically animage with some controlled keywords that can be used for indexing and retrieval of images. This paper presents a comparative evaluation of the image content annotation system by using the multilayer neural networks and the nearest neighbour classifier. The region growing segmentation is used to separate objects, the Hu moments, Legendre moments and Zernike moments which are used in as feature descriptors for the image content characterization and annotation.The ETH-80 database image is used in the experiments here. The best annotation rate is achieved by using Legendre moments as feature extraction method and the multilayer neural network as a classifier.

Mustapha OUJAOURA,, Brahim MINAOUI and Mohammed FAKIR, “Multilayer Neural Networks and Nearest Neighbor Classifier Performances for Image Annotation” International Journal of Advanced Computer Science and Applications(IJACSA), 3(11), 2012. http://dx.doi.org/10.14569/IJACSA.2012.031126

@article{OUJAOURA,2012,
title = {Multilayer Neural Networks and Nearest Neighbor Classifier Performances for Image Annotation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2012.031126},
url = {http://dx.doi.org/10.14569/IJACSA.2012.031126},
year = {2012},
publisher = {The Science and Information Organization},
volume = {3},
number = {11},
author = {Mustapha OUJAOURA, and Brahim MINAOUI and Mohammed FAKIR}
}