DocumentCode :
3672448
Title :
Image retrieval using scene graphs
Author :
Justin Johnson;Ranjay Krishna;Michael Stark;Li-Jia Li;David A. Shamma;Michael S. Bernstein;Li Fei-Fei
Author_Institution :
Stanford University, USA
fYear :
2015
fDate :
6/1/2015 12:00:00 AM
Firstpage :
3668
Lastpage :
3678
Abstract :
This paper develops a novel framework for semantic image retrieval based on the notion of a scene graph. Our scene graphs represent objects (“man”, “boat”), attributes of objects (“boat is white”) and relationships between objects (“man standing on boat”). We use these scene graphs as queries to retrieve semantically related images. To this end, we design a conditional random field model that reasons about possible groundings of scene graphs to test images. The likelihoods of these groundings are used as ranking scores for retrieval. We introduce a novel dataset of 5,000 human-generated scene graphs grounded to images and use this dataset to evaluate our method for image retrieval. In particular, we evaluate retrieval using full scene graphs and small scene subgraphs, and show that our method outperforms retrieval methods that use only objects or low-level image features. In addition, we show that our full model can be used to improve object localization compared to baseline methods.
Keywords :
"Grounding","Semantics","Image retrieval","Visualization","Boats","Computational modeling","Context"
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2015.7298990
Filename :
7298990
Link To Document :
بازگشت