大规模图像语义相关性自动标注

摘要

针对大规模图像集合的自动标注问题,提出一种图像语义相关性自动标注方法.首先提取图像的视觉特征,将每个样本表示为局部邻域样本点的稀疏线性组合；然后采用一种基于最大后验概率准则的多标签学习方法得到每幅图像的单特征标签相关度；最终对单个特征和特定标签的相关度阈值进行无偏估计,并采用无监督组合方法融合多种视觉特征和标签的相关度.互联网数据集测试结果表明,该方法是有效的.%An image semantic relevance annotation method is proposed, which aims at tagging large-scale image collections in real environment. First, in specific feature space, each training image is encoded as a sparse linear combination of other training images by sparse representation. Then a multiple label learning method based on maximum a posteriori principle is utilized to generate the relevance of tags and images in specific feature space. Finally, an optimal threshold set for each tag and corresponding feature is estimated, and the tag relevance of diverse features can be combined in the manner of unsupervised combination. The experiments on the Internet image set show the superior performance of the proposed framework.