Skip to Main content Skip to Navigation
Conference papers

Description of interest regions with center-symmetric local binary patterns

Marko Heikkila 1 Matti Pietikainen 1 Cordelia Schmid 2, * 
* Corresponding author
2 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : Local feature detection and description have gained a lot ofinterest in recent years since photometric descriptors computed for interest regions have proven to be very successful in many applications. In this paper, we propose a novel interest region descriptor which combines the strengths of the well-known SIFT descriptor and the LBP texture operator. It is called the center-symmetric local binary pattern (CS-LBP) descriptor. This new descriptor has several advantages such as tolerance to illumination changes, robustness on flat image areas, and computational efficiency. We evaluate our descriptor using a recently presented test protocol. Experimental results show that the CS-LBP descriptor outperforms the SIFT descriptor for most of the test cases, especially for images with severe illumination variations.
Document type :
Conference papers
Complete list of metadata
Contributor : THOTH Team Connect in order to contact the contributor
Submitted on : Monday, December 20, 2010 - 9:49:27 AM
Last modification on : Wednesday, February 2, 2022 - 3:58:36 PM

Links full text




Marko Heikkila, Matti Pietikainen, Cordelia Schmid. Description of interest regions with center-symmetric local binary patterns. 5th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP '06), Dec 2006, Madurai, India. pp.58--69, ⟨10.1007/11949619_6⟩. ⟨inria-00548586⟩



Record views