earticle

영어

Automatic image annotation has been an active research topic in recent years due to its potential impact on both image understanding and semantic based image retrieval. However, the results of the state-of-the-art image annotation methods are still far from satisfaction due to the existence of semantic gap. Thus refining image annotation (RIA) has become one of the core research topics in computer vision and multimedia areas, whose purpose is to reserve the highly correlated annotations whereas remove the irrelevant or weakly relevant annotations by fully exploring the correlations of annotation keywords. RIA, to some extent, can effectively mitigate the semantic gap between low-level visual features and high-level semantic concepts. So in this paper, we focus on the latest development in image retrieval and provide a comprehensive survey on refining image annotation techniques. In particular, we analyze the key aspects of various RIA methods, including their original intentions and annotation models. Finally, we draw some important conclusions and highlight the potential research directions for the future.