A sparse texture representation using local affine regions

Svetlana Lazebnik, Cordelia Schmid, Jean Ponce

Research output: Contribution to journalReview article

Abstract

This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extraction stage, a sparse set of affine Harris and Laplacian regions is found in the image. Each of these regions can be thought of as a texture element having a characteristic elliptic shape and a distinctive appearance pattern. This pattern is captured in an affine-invariant fashion via a process of shape normalization followed by the computation of two novel descriptors, the spin image and the RIFT descriptor. When affine invariance is not required, the original elliptical shape serves as an additional discriminative feature for texture recognition. The proposed approach is evaluated in retrieval and classification tasks using the entire Brodatz database and a publicly available collection of 1,000 photographs of textured surfaces taken from different viewpoints.

Original languageEnglish (US)
Pages (from-to)1265-1278
Number of pages14
JournalIEEE transactions on pattern analysis and machine intelligence
Volume27
Issue number8
DOIs
StatePublished - Aug 2005

Keywords

  • Feature measurement
  • Image processing and computer vision
  • Pattern recognition
  • Texture

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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