Skip to Main content Skip to Navigation
Conference papers

K-centroids based supervised classification of texture images: handling the intra-class diversity

Abstract : Natural texture images exhibit a high intra-class diversity due to different acquisition conditions (scene enlightenment, perspective angle, . . . ). To handle with the diversity, a new supervised classification algorithm based on a parametric formalism is introduced: the K-centroids-based classifier (K-CB). A comparative study between various supervised classification algorithms on the VisTex and Brodatz image databases is conducted and reveals that the proposed K-CB classifier obtains relatively good classification accuracy with a low computational complexity.
Complete list of metadata

Cited literature [14 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00841939
Contributor : Lionel Bombrun Connect in order to contact the contributor
Submitted on : Friday, July 5, 2013 - 5:29:20 PM
Last modification on : Wednesday, January 31, 2018 - 1:46:02 PM
Long-term archiving on: : Sunday, October 6, 2013 - 4:17:38 AM

File

Schutz13_ICASSP.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00841939, version 1

Citation

Aurélien Schutz, Lionel Bombrun, Yannick Berthoumieu. K-centroids based supervised classification of texture images: handling the intra-class diversity. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013, Vancouver, Canada. pp.1498-1502. ⟨hal-00841939⟩

Share

Metrics

Record views

279

Files downloads

352