Texel-based image classification with orthogonal bases

Erik Carbajal-Degante; Rodrigo Nava; Jimena Olveres; Boris Escalante-Ramírez; Jan Kybic

doi:10.1117/12.2228694

29 April 2016 Texel-based image classification with orthogonal bases

Erik Carbajal-Degante, Rodrigo Nava, Jimena Olveres, Boris Escalante-Ramírez, Jan Kybic

Proceedings Volume 9896, Optics, Photonics and Digital Technologies for Imaging Applications IV; 98961K (2016) https://doi.org/10.1117/12.2228694
Event: SPIE Photonics Europe, 2016, Brussels, Belgium

Abstract

Periodic variations in patterns within a group of pixels provide important information about the surface of interest and can be used to identify objects or regions. Hence, a proper analysis can be applied to extract particular features according to some specific image properties. Recently, texture analysis using orthogonal polynomials has gained attention since polynomials characterize the pseudo-periodic behavior of textures through the projection of the pattern of interest over a group of kernel functions. However, the maximum polynomial order is often linked to the size of the texture, which implies in many cases, a complex calculation and introduces instability in higher orders leading to computational errors. In this paper, we address this issue and explore a pre-processing stage to compute the optimal size of the window of analysis called “texel.” We propose Haralick-based metrics to find the main oscillation period, such that, it represents the fundamental texture and captures the minimum information, which is sufficient for classification tasks. This procedure avoids the computation of large polynomials and reduces substantially the feature space with small classification errors. Our proposal is also compared against different fixed-size windows. We also show similarities between full-image representations and the ones based on texels in terms of visual structures and feature vectors using two different orthogonal bases: Tchebichef and Hermite polynomials. Finally, we assess the performance of the proposal using well-known texture databases found in the literature.

Citation Download Citation

Erik Carbajal-Degante, Rodrigo Nava, Jimena Olveres, Boris Escalante-Ramírez, and Jan Kybic "Texel-based image classification with orthogonal bases", Proc. SPIE 9896, Optics, Photonics and Digital Technologies for Imaging Applications IV, 98961K (29 April 2016); https://doi.org/10.1117/12.2228694

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Error analysis

Matrices

Databases

Image classification

Fourier transforms

Statistical analysis

Show All Keywords

Keywords/Phrases

Search In:

Publication Years