Texture classification with thousands of features | Zendy

A. Kadyrov | Zendy; A. Talepbour | Zendy; M. Petrou | Zendy

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Texture classification with thousands of features

Author(s) -

A. Kadyrov,

A. Talepbour,

M. Petrou

Publication year - 2002

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5244/c.16.64

Subject(s) - computer science , artificial intelligence , texture (cosmology) , construct (python library) , image (mathematics) , radon transform , trace (psycholinguistics) , task (project management) , pattern recognition (psychology) , perception , image texture , feature extraction , computer vision , meaning (existential) , orthogonality , measure (data warehouse) , matrix (chemical analysis) , image processing , mathematics , data mining , engineering , psychology , linguistics , philosophy , materials science , geometry , systems engineering , composite material , neuroscience , psychotherapist , biology , programming language

The Trace transform is a generalisation of the Radon transform that allows one to construct image features that do not necessarily have meaning in terms of human perception, but they measure different image characteristics. The ability of producing thousands of features from an image allows one to be selective as to which are appropriate for a particular task. In this paper we propose the use of such an approach to the problem of texture discrimination and compare its results with the classical co-occurrence matrix approach where usually the features used are fewer than ten.

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