Authors:
Pascal Mettes
;
Robby Tan
and
Remco Veltkamp
Affiliation:
Utrecht University, Netherlands
Keyword(s):
Material Classification, Class-dependent Selection, Feature Selection, Polar Grids, Feature-space Weighting
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
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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