A Bottom-up Approach to Class-dependent Feature Selection for Material Classification

Pascal Mettes, Robby Tan, Remco Veltkamp

Abstract

In this work, the merits of class-dependent image feature selection for real-world material classification is investigated. Current state-of-the-art approaches to material classification attempt to discriminate materials based on their surface properties by using a rich set of heterogeneous local features. The primary foundation of these approaches is the hypothesis that materials can be optimally discriminated using a single combination of features. Here, a method for determining the optimal subset of features for each material category separately is introduced. Furthermore, translation and scale-invariant polar grids have been designed in this work to show that, although materials are not restricted to a specific shape, there is a clear structure in the spatial allocation of local features. Experimental evaluation on a database of real-world materials indicates that indeed each material category has its own preference. The use of both the class-dependent feature selection and polar grids results in recognition rates which exceed the current state-of-the-art results.

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Paper Citation


in Harvard Style

Mettes P., Tan R. and Veltkamp R. (2014). A Bottom-up Approach to Class-dependent Feature Selection for Material Classification . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 494-501. DOI: 10.5220/0004721204940501


in Bibtex Style

@conference{visapp14,
author={Pascal Mettes and Robby Tan and Remco Veltkamp},
title={A Bottom-up Approach to Class-dependent Feature Selection for Material Classification},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={494-501},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004721204940501},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - A Bottom-up Approach to Class-dependent Feature Selection for Material Classification
SN - 978-989-758-004-8
AU - Mettes P.
AU - Tan R.
AU - Veltkamp R.
PY - 2014
SP - 494
EP - 501
DO - 10.5220/0004721204940501