Mustapha Oujaoura

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This document presents a system in order to annotate image content by using the region growing segmentation, as a method to separate different objects within an image, and the multilayer neural network to classify these objects and to find the appropriate keywords for them. In many applications, different kinds of moments have been used as features to(More)
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(More)
Most of the reported works in the field of character recognition systems achieve modest results by using a single method for calculating the parameters of the character image and a single approach in the classification phase of the system. So, in order to improve the recognition rate, this document proposes an automatic system to recognize isolated printed(More)
The aim of this work is to present a system for recognizing isolated Arabic printed characters. This system goes through several stages: preprocessing, feature extraction and classification. Zernike moments, invariant moments and Walsh transformation are used to calculate the features. The classification is based on multilayer neural networks. A recognition(More)
In order to improve the recognition rate, this document proposes an automatic system to recognize isolated printed Tifinagh characters by using a fusion of 3 classifiers and a combination of some features extraction methods. The Legendre moments, Zernike moments and Hu moments are used as descriptors in the features extraction phase due to their invariance(More)
Many features extraction method and classifiers are used singly, with modest results, for automatic image annotation. In order to improve the semantic image annotation accuracy, this document provides an automatic system to annotate image content by using a fusion of 3 classifier and a combination of some features extraction methods; multiclass support(More)
In this paper, we propose a hybrid approach based on neural networks and the combination of the classic Hu & Zernike moments joined with Geodesic descriptors. To be able to keep the maximum amount of information that are given by the color of the image, we have calculated Zernike & Hu for each color level. On the other side, geodesic descriptors are(More)
Due to the large amounts of multimedia data prevalent on the Web, Some images presents textural motifs while others may be recognized with colors or shapes of their content. The use of descriptors based on one’s features extraction method, such as color or texture or shape, for automatic image annotation are not efficient in some situations or in absence of(More)