Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR) systems especially with the rapid growth of the number of online documents present in Arabic language. Documents clustering aim to automatically group similar documents in one cluster using different similarity/distance measures. This task is often affected by the documents length, useful information on the documents is often accompanied by a large amount of noise, and therefore it is necessary to eliminate this noise while keeping useful information to boost the performance of Documents clustering. In this paper, we propose to evaluate the impact of text summarization using the Latent Semantic Analysis Model on Arabic Documents Clustering in order to solve problems cited above, using five similarity/distance measures: Euclidean Distance, Cosine Similarity, Jaccard Coefficient, Pearson Correlation Coefficient and Averaged Kullback-Leibler Divergence, for two times: without and with stemming. Our experimental results indicate that our proposed approach effectively solves the problems of noisy information and documents length, and thus significantly improve the clustering performance.

To realize a system for textile design patterns retrieval, we adopt an Image Indexing MethodBased Region. This indexing method is achieved by regions segmentation process followed by regions indexing one. Note that the later indexing process strongly depends on the quality of produced segmentation, and will have a negative impact on retrieval results. Therefore, an efficient segmentation method must be developed. In this paper, we propose an adaptive and efficient unsupervised color image segmentation method. The proposed method is based on Gaussian Mixture Model, and cluster validity index. The model parameters are estimated using EM algorithm, and the optimal cluster number is detected by Cluster Validity Index. Using this method, the design patterns are well extracted. In the second part of this work, the outcome of this segmentation results will be supplied to the input of the indexing process to complete our retrieval system.

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