摘要
基于草图的图像检索任务根据用户提供的手绘草图,从图像数据库中检索得到与该草图对应的自然图像.与传统基于内容的图像检索不同,草图和自然图像间存在明显的域差异,这使得二者的特征难以直接进行比较.针对自然图像边缘图和草图的相似性,提出了空间注意力下的边缘图融合模型,将自然图像和对应的边缘图分别编码到各自的特征空间,再通过空间注意力掩膜进行加权融合,进而用于草图图像检索.所提模型可以更有效地编码物体边缘轮廓的特征,分别在Sketchy和Flickr15K数据集的草图图像检索任务上取得了比前人方法更高的Recall@1和MeanAP指标.
Sketch-based image retrieval(SBIR)aims to return a collection of corresponding images based on an input sketch.Different from traditional content-based image retrieval,unique difficulties exist due to the large domain gap between sketches and natural images.Based on the similarity between edgemaps and sketches,a novel SBIR model named spatial attentive edgemap fusion is presented which combines both image and edgemap features.Images and the corresponding edgemaps are first encoded to their own latent feature space,and then fused by a learned spatial attention map.Experiment results on two widely-used SBIR datasets,Sketchy and Flickr15K,show the promising performance of the proposed model.
作者
郭元晨
蔡韵
张松海
Guo Yuanchen;Cai Yun;Zhang Songhai(Department of Computer Science and Technology,Tsinghua University,Beijing 100084)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2021年第6期847-854,共8页
Journal of Computer-Aided Design & Computer Graphics
基金
国家重点研发计划(2017YFB1002604)
国家自然科学基金(61772298,61832016)。
