10.1184/R1/6552491.v1
Abhinav Shrivastava
Abhinav
Shrivastava
Tomasz Malisiewicz
Tomasz
Malisiewicz
Abhinav Gupta
Abhinav
Gupta
Alexei Efros
Alexei
Efros
Data-driven Visual Similarity for Cross-domain Image Matching
Carnegie Mellon University
2018
image matching
visual similarity
saliency
image retrieval
paintings
sketches
re-photography
visual memex
2018-06-30 01:15:05
Journal contribution
https://kilthub.cmu.edu/articles/journal_contribution/Data-driven_Visual_Similarity_for_Cross-domain_Image_Matching/6552491
<p>The goal of this work is to find visually similar images even if they appear quite different at the raw pixel level. This task is particularly important for matching images across visual domains, such as photos taken over different seasons or lighting conditions, paintings, hand-drawn sketches, etc. We propose a surprisingly simple method that estimates the relative importance of different features in a query image based on the notion of "data-driven uniqueness". We employ standard tools from discriminative object detection in a novel way, yielding a generic approach that does not depend on a particular image representation or a specific visual domain. Our approach shows good performance on a number of difficult cross-domain visual tasks e.g., matching paintings or sketches to real photographs. The method also allows us to demonstrate novel applications such as Internet re-photography, and painting2gps.</p>