The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

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.

Keywords

image matching, visual similarity, saliency, image retrieval, paintings, sketches, re-photography, visual memex

Notes

Grant ID: New York, NY, USA

Associated Lab(s) / Group(s): Computer Graphics Lab

Number of pages: 9

Text Reference

Abhinav Shrivastava, Tomasz Malisiewicz, Abhinav Gupta, and Alexei A. Efros, "Data-driven visual similarity for cross-domain image matching," ACM Transactions on Graphics (TOG)- Proceedings of ACM SIGGRAPH Asia 2011, Vol. 30, No. 6, December, 2011

BibTeX Reference

@article{Shrivastava_2011_7327,
   author = "Abhinav Shrivastava and Tomasz Malisiewicz and Abhinav Gupta and Alexei A. Efros",
   title = "Data-driven visual similarity for cross-domain image matching",
   journal = "ACM Transactions on Graphics (TOG)- Proceedings of ACM SIGGRAPH Asia 2011",
   publisher = "ACM",
   address = "New York, NY, USA",
   month = "December",
   year = "2011",
   volume = "30",
   number = "6",
}

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