With the emergence of massive shared images in the Web2.0 era

it has become a significant research topic to get precise descriptive region-level annotations for images. In this paper

we propose a new image annotation algorithm based on region-semantic diverse density

focusing on the differences of visual feature and spatial structure among regions. In details

the algorithm uses diverse density method based on feature distance similarity and region spatial location

and introducing color

shape together with texture property annotations. Experiments on parts of the NUS-WIDE and MSRC datasets demonstrate that the proposed method is effective. The accuracy is more than 80% in property annotations. Furthermore

the average precision of image retrieval using property annotations is up to 82%. The experiments results show that the proposed image annotation framework can get relevant semantic regions and property annotations more accurately

and effectively solves the problem of regional annotation.