Abstract
Automated image annotation (AIA) is an important issue in computer vision and pattern recognition, and plays an extremely important role in retrieving large-scale images. In many image annotation approaches, different regions of the image are processed equally, which is inconsistent with the mechanism by which humans understand images. In order to improve the annotation performance of existing AIA approaches, a hybrid AIA approach based on visual attention mechanism (VAM) and the conditional random field (CRF) is proposed. First, since people pay more attention to the salient region of an image during the image recognition process, VAM is implemented for acquiring the salient and non salient regions of the image. Second, support vector machine (SVM) is used to annotate the salient region, and k nearest neighbor (kNN) voting algorithm is used to annotate the non salient regions. Finally, due to the existence of a certain relationship between any two annotation words (also called labels), CRF is calculated to obtain the final label set of each given image. The experimental results confirm that the proposed hybrid AIA approach has ideal annotation performance.
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