By Topic

A Scene Images Classification Method Based on Local Binary Patterns and Nearest-Neighbor Classifier

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Guang Han ; Coll. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjingz ; Chunxia Zhao

Classification of textures in scene images is very difficult due to the high variability of the data within and between images caused by effects such as non-homogeneity of the textures, changes in illumination, shadows, foreshortening and self-occlusion. For these reasons, finding proper features and representative training samples for a classifier is very problematic. Even defining the classes that can be discriminated with texture information is not so straightforward. In this paper, a visualization-based approach for training a texture classifier is presented. A improved multi-channel local binary patterns (LBP) in RGB color space are used as textured color features and a K-NN is employed for visual training and classification, providing very promising results in the classification of outdoor scene images.

Published in:

2008 Eighth International Conference on Intelligent Systems Design and Applications  (Volume:1 )

Date of Conference:

26-28 Nov. 2008