A bottom-up approach to class-dependent feature selection for material classification

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.