SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches
Fang Liu, Changqing Zou, Xiaoming Deng, Ran Zuo, Yu-Kun Lai, Cuixia Ma, Yong-Jin Liu, Hongan Wang
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."
BibTeX
@inproceedings{eccv2020_scenesketcherfin,
title = {SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches},
author = {Fang Liu and Changqing Zou and Xiaoming Deng and Ran Zuo and Yu-Kun Lai and Cuixia Ma and Yong-Jin Liu and Hongan Wang},
booktitle = {ECCV 2020},
year = {2020}
}