Experimental studies of visual models in automatic image annotation

Abstract

Semantic image annotation can be viewed as a mapping procedure from image features to semantic labels, by the steps of image feature extraction and image-semantic mapping. The features can be low-level visual features, such as color, texture, shape, etc., and the semantic labels can be related to the knowledge of human on the image understanding. However, these linear representations are insufficient to describe the complex natural scene. In this paper, we study currently existing visual models that are able to imitate the way the human visual system acts for the tasks of object recognition and scene interpretation. Therefore, it is expected to bring a better understanding to the image visual content in human cortex will. In the experiments, there are three state-of-the-art visual models are investigated for the application of automatic image annotation. The results demonstrate that with our proposed strategy, the annotation accuracy is improved comparing to the most used low-level linear representation features. © 2011 Springer-Verlag.