Automatic image annotation(AIA) is an important and challenging job for image analysis and understanding such as Content-Based Image Retrieval(CBIR). In AIA

a model for annotation which represents the relationship between the semantic concept space and the visual feature space

is constructed by learning from annotated image datasets. The performance of the AIA is still poor because the relationship between the key words and visual features is too compli-cated due to the semantic gap. However

with constrains under the scenes

the correlation between them becomes simpler and clearer for better annotation results. In this paper

a method of automatic image annotation based on scene semantic tree is proposed. Image scenes are obtained through the annotated words using PLSA. Scene semantic trees are constructed for each image scene. Un-annotated images are classified into certain scenes with visual features and are annotated using the corresponding scene semantic tree. Using the visual features provided by Duygulu

the experiments get the more effective results on Corel5K database than TM

CMRM

CRM and PLSA-GMM.