SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches
Abstract
Sketch-based image retrieval (SBIR) has been a popular research topic in recent years. Existing works concentrate on mapping the visual information of sketches and images to a semantic space at the object level. In this paper, for the first time, we study the fine-grained scene-level SBIR problem which aims at retrieving scene images satisfying the user's specific requirements via a freehand scene sketch. We propose a graph embedding based method to learn the similarity measurement between images and scene sketches, which models the multi-modal information, including the size and appearance of objects as well as their layout information, in an effective manner. To evaluate our approach, we collect a dataset based on SketchyCOCO and extend the dataset using Coco-stuff. Comprehensive experiments demonstrate the significant potential of the proposed approach on the application of fine-grained scene-level image retrieval.
Cite
Text
Liu et al. "SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58529-7_42Markdown
[Liu et al. "SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/liu2020eccv-scenesketcher/) doi:10.1007/978-3-030-58529-7_42BibTeX
@inproceedings{liu2020eccv-scenesketcher,
title = {{SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches}},
author = {Liu, Fang and Zou, Changqing and Deng, Xiaoming and Zuo, Ran and Lai, Yu-Kun and Ma, Cuixia and Liu, Yong-Jin and Wang, Hongan},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58529-7_42},
url = {https://mlanthology.org/eccv/2020/liu2020eccv-scenesketcher/}
}