Spreading activation theory based image annotation

The overwhelming amounts of digital images on the Web and personal computers have triggered the requirement of an effective tool to retrieve images of interest using semantic concepts. Due to the semantic gap between low level content features and its high level semantic features of an image, however, the performances of many existing automatic image annotation algorithms are not so satisfactory. In this paper, a novel approach based on the cognitive science is proposed to improve the quality of annotations. The main idea is that the tags of an image are considered as nodes in a semantic network, and the relevance between the tags and image contents is regulated using the spreading activation theory. After the spreading activation process finishes, each tag will be assigned an appropriate value with respect to its relation to other tags. Experimental results conducted on the 50,000 Flickr images demonstrate that the proposed scheme can effectively improve the performance in automatic image annotation.