Title :
Texture classification with minimal training images
Author :
Targhi, Alireza Tavakoli ; Geusebroek, Jan-Mark ; Zisserman, Andrew
Author_Institution :
CVAP, KTH, Stockholm
Abstract :
The objective of this work is classifying texture from a single image under unknown lighting conditions. The current and successful approach to this task is to treat it as a statistical learning problem and learn a classifier from a set of training images, but this requires a sufficient number and variety of training images. We show that the number of training images required can be drastically reduced (to as few as three) by synthesizing additional training data using photometric stereo. We demonstrate the method on the PhoTex and ALOT texture databases. Despite the limitations of photometric stereo, the resulting classification performance surpasses the state of the art results.
Keywords :
image classification; image texture; learning (artificial intelligence); statistical analysis; stereo image processing; ALOT texture database; PhoTex texture database; image texture classification; lighting condition; minimal training image; photometric stereo; statistical learning problem; Availability; Filters; Image databases; Image generation; Lighting; Photometry; Statistical learning; Surface texture; Surface treatment; Training data;
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
DOI :
10.1109/ICPR.2008.4761388