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Feature Tracking for Wide-Baseline Image Retrieval

  • Ameesh Makadia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

Abstract

We address the problem of large scale image retrieval in a wide-baseline setting, where for any query image all the matching database images will come from very different viewpoints. In such settings traditional bag-of-visual-words approaches are not equipped to handle the significant feature descriptor transformations that occur under large camera motions. In this paper we present a novel approach that includes an offline step of feature matching which allows us to observe how local descriptors transform under large camera motions. These observations are encoded in a graph in the quantized feature space. This graph can be used directly within a soft-assignment feature quantization scheme for image retrieval.

Keywords

Image Retrieval Visual Word Query Image Feature Tracking Graph Construction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ameesh Makadia
    • 1
  1. 1.Google ResearchNew York

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