  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
(Na\"ive Bayes, Global Tree Augmented Na\"ive Bayes (GTAN), Global Forest
Augmented Na\"ive Bayes (GFAN), Tree Augmented Na\"ive Bayes for each class
(TAN), and Forest Augmented Na\"ive Bayes for each class (FAN) to classify the
image of faces using the vector of labels. In order to validate the feasibility
and effectively, we compare the results of GFAN to FAN and to the others
classifiers (NB, GTAN, TAN). The results demonstrate FAN outperforms than GFAN,
NB, GTAN and TAN in the overall classification accuracy.
