Automatic image content annotation techniques attempt to explore structural visual features of images that describe image content and associate them with image semantics. In this paper, two types of concept spaces, atomic concept and collective concept spaces, are defined and the annotation problems in those spaces are formulated as feature classification and Bayesian inference, respectively. A scheme of image content annotation in this framework is presented and evaluated as an application of photo categorisation using MPEG-7 VCE2 dataset and its ground truth. The experimental results show a promising performance.
['Lei Ye', 'Philip Ogunbona', 'Jianqiang Wang']
Image Content Annotation Based on Visual Features