Dictionary Learning in Texture Classification View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011

AUTHORS

Mehrdad J. Gangeh , Ali Ghodsi , Mohamed S. Kamel

ABSTRACT

Texture analysis is used in numerous applications in various fields. There have been many different approaches/techniques in the literature for texture analysis among which the texton-based approach that computes the primitive elements representing textures using k-means algorithm has shown great success. Recently, dictionary learning and sparse coding has provided state-of-the-art results in various applications. With recent advances in computing the dictionary and sparse coefficients using fast algorithms, it is possible to use these techniques to learn the primitive elements and histogram of them to represent textures. In this paper, online learning is used as fast implementation of sparse coding for texture classification. The results show similar to or better performance than texton based approach on CUReT database despite of computation of dictionary without taking into account the class labels. More... »

PAGES

335-343

Book

TITLE

Image Analysis and Recognition

ISBN

978-3-642-21592-6
978-3-642-21593-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-21593-3_34

DOI

http://dx.doi.org/10.1007/978-3-642-21593-3_34

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1005310436


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