针对自动图像标注中底层特征和高层语义之间的鸿沟问题,提出一种基于随机点积图的图像标注改善算法。该算法首先采用图像底层特征对图像候选标注词建立语义关系图,然后利用随机点积图对其进行随机重构,从而挖掘出训练图像集中丢失的语义关系,最后采用重启式随机游走算法,实现图像标注改善。该算法结合了图像的底层特征与高层语义,有效降低了图像集规模变小对标注的影响。在3种通用图像库上的实验证明了该算法能够有效改善图像标注,宏F值与微平均F值最高分别达到0.784与0.743。

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.