Improving Local Descriptors by Embedding Global and Local Spatial Information View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010

AUTHORS

Tatsuya Harada , Hideki Nakayama , Yasuo Kuniyoshi

ABSTRACT

In this paper, we present a novel problem: “Given local descriptors, how can we incorporate both local and global spatial information into the descriptors, and obtain compact and discriminative features?” To address this problem, we proposed a general framework to improve any local descriptors by embedding both local and global spatial information. In addition, we proposed a simple and powerful combination method for different types of features. We evaluated the proposed method for the most standard scene and object recognition dataset, and confirm the effectiveness of the proposed method from the viewpoint of speed and accuracy. More... »

PAGES

736-749

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1997-11. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss in MACHINE LEARNING
  • 2006. Region Covariance: A Fast Descriptor for Detection and Classification in COMPUTER VISION – ECCV 2006
  • 2006. Probabilistic Linear Discriminant Analysis in COMPUTER VISION – ECCV 2006
  • 2001-05. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Book

    TITLE

    Computer Vision – ECCV 2010

    ISBN

    978-3-642-15560-4
    978-3-642-15561-1

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15561-1_53

    DOI

    http://dx.doi.org/10.1007/978-3-642-15561-1_53

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1021051243


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