Clustering and Bayesian network for image of faces classification
Khlifia Jayech, Mohamed Ali · International Journal of Advanced Computer Science and Applications · 2011
In a content based image classification system, target images are sorted by feature similarities with respect to the query (CBIR). In this paper, we propose to use new approach combining distance tangent, k-means algorithm and Bayesian network for image classification. First, we use the technique of tangent distance to calculate several tangent spaces representing the same image. The objective is to reduce the error in the classification phase. Second, we cut the image in a whole of blocks. For each block, we compute a vector of descriptors. Then, we use K-means to cluster the low-level features including color and texture information to build a vector of labels for each image. Finally, we apply five variants of Bayesian networks classifiers ()aïve Bayes, Global Tree Augmented )aïve Bayes (GTA)), Global Forest Augmented )aïve Bayes (GFA)), Tree Augmented )aïve Bayes for each class (TA)), and Forest Augmented )aïve Bayes for each class (FA)) to classify the image of faces using the vector of labels. In order to validate the feasibility and effectively, we compare the results of GFA) to FA) and to the others classifiers ()B, GTA), TA)). The results demonstrate FA) outperforms than GFA), )B, GTA) and TA) in the overall classification accuracy.