Earticle

초록

영어

It is shown that distance computations between SIFT-descriptors using the Euclidean distance suffer from the curse of dimensionality. The search for exact matches is less affected than the generalisation of image patterns, e.g. by clustering methods. Experimental results indicate that for the case of generalisation, the Hamming distance on binarised SIFTdescriptors is a much better choice. It is shown that the binary feature representation is visually plausible, numerically stable and information preserving. In an histogram-based object recognition system, the binary representation allows for the quick matching, compact storage and fast training of a code-book of features. A time-consuming clustering of the input data is redundant.

목차

Abstract
 1. Introduction
 2. Distance between SIFT-descriptors
 3. Binarisation of feature descriptors
 4. Correspondence between binarised descriptors and the image contents
 5. Application to a visual code-book for object recognition
 6. Image classification using a histogram of binarised descriptors
 7. Conclusion
 References

키워드

저자

• Martin Stommel [ TZI Center for Computing and Communication Technologies University Bremen ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605