基于视觉注意力和FCA的古建筑图像语义完备

Semantic Completion Annotation of Ancient Architecture Based on Visual Attention Mechanism and FCA

准确完备的古建筑图像语义不仅可提高古建筑图像检索效率,且能有效反映古建筑的历史文化信息。针对不同古建筑图像轮廓特征明显不同且建筑语义互相关联,为有效丰富古建筑图像语义,提出一种基于视觉注意力机制和形式概念分析(Formal Concept Analysis,FCA)的古建筑图像语义完备方法。首先使用注意力算子网络和VGG16网络模型生成待标注古建筑图像注意力图,并通过softmax分类器进行分类,获取图像初始标签集;其次构造基于待标注图像初始标签及其近邻标签的概念格;然后,利用概念格上下文分析语义的特点,通过概念节点之间的相似度度量,获取待标注图像潜在的语义标签。最后,在古建筑图像数据集上进行实验,结果验证了该方法能够有效地提高古建筑图像标注精度,丰富古建筑图像语义。

Accurate and complete ancient architectural image semantics can improve the efficiency of image retrieval and effectively reflect the historical and cultural information.Aiming at the problem that the contour features of different ancient architectural images are different and the architectural semantics are interrelated,we propose a semantic completion method of ancient architectural images based on visual attention mechanism and FCA(Formal Concept Analysis),which effectively enriches image semantics.Firstly,the attention operator network and VGG16 network model are used to generate the attention map of ancient architecture,and the initial label set of the image is obtained by softmax classifier.Secondly,we construct a concept lattice based on the initial label of the image to be labeled and its neighbor labels.Then we use the context of the concept lattice to analyze the semantic characteristics and obtain the latent semantic labels of the image to be labeled by measuring the similarity between the concept nodes.Finally,experiments on the ancient architecture image datasets show that the proposed method can effectively complete the semantics of the ancient architecture image and improve the accuracy of image annotation.