Combining Textural and Geometrical Descriptors for Scene Recognition

Neslihan Bayramolu, Janne Heikkilä, and Matti Pietikäinen

Abstract. Local description of images is a common technique in many computer vision related research. Due to recent improvements in RGB-D cameras, local description of 3D data also becomes practical. The number of studies that make use of this extra information is increasing. However, their applicabilities are limited due to the need for generic combination methods. In this paper, we propose combining textural and geometrical descriptors for scene recognition of RGB-D data. The methods together with the normalization stages proposed in this paper can be applied to combine any descriptors obtained from 2D and 3D domains. This study represents and evaluates different ways of combining multi-modal descriptors within the BoW approach in the context of indoor scene localization. Query’s rough location is determined from the pre-recorded images and depth maps in an unsupervised image matching manner.

Keywords: 2D/3D description, feature fusion, localization

LNCS 7584, p. 32 ff.

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