Content and context-based multi-label image annotation

In this paper, we propose a multi-label image annotation framework by incorporating the content and context information of images. Specifically, images are annotated on regional scale. This annotation is independent of the sizes of blocks. Confidences of content-based block and image annotation are then obtained. On the other hand, spatial features by combining the block annotation confidence and the spatial context are proposed for main concepts, corresponding to the concepts been annotated, and the auxiliary concepts, corresponding to the concepts that have high co-occurrence with the main concepts in the images. This proposed spatial feature can incorporate the position of the concept and the spatial context between these concepts. Experiments on expanded Corel dataset categories demonstrate the effectiveness of the proposed method.