Image annotation refinement based on a random dot product graph
基于随机点积图的图像标注改善算法

In order to overcome the semantic gap between low-level features and high-level semantic concepts of imagery, a new image annotation refinement approach based on Random Dot Product Graph (RDPG)is proposed. In our approach, the visual features of images are used to construct a semantic graph of the candidate annotations. Then, we reconstruct the semantic graph with a RDPG, find the unobserved relevance in the incompletely observed semantic graph, and transform the random graph into the probabilities of state transition. Combined with Random Walk with Restart (RWR), the final annotations are chosen. This new method incorporates the visual and semantic information of images, and reduces the influence of the scale of database. Experiments conducted on three standard databases demonstrate that our approach outperforms the existing image annotation refinement techniques. The macro F-Score and micro average F-Score can reach 0.784 and 0.743 respectively.