Abstract: Recently it has become essential to search for and retrieve high-resolution
and efficient images easily due to swift development of digital images, many
present annotation algorithms facing a big challenge which is the variance for
represent the image where high level represent image semantic and low level
illustrate the features, this issue is known as semantic gab. This work has
been used MPEG-7 standard to extract the features from the images, where the
color feature was extracted by using Scalable Color Descriptor (SCD) and Color
Layout Descriptor (CLD), whereas the texture feature was extracted by employing
Edge Histogram Descriptor (EHD), the CLD produced high dimensionality feature
vector therefore it is reduced by Principal Component Analysis (PCA). The
features that have extracted by these three descriptors could be passing to the
classifiers (Naive Bayes and Decision Tree) for training. Finally, they
annotated the query image. In this study TUDarmstadt image bank had been used.
The results of tests and comparative performance evaluation indicated better
precision and executing time of Naive Bayes classification in comparison with
Decision Tree classification.