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Indoor scene classification has become an important task in perceptionmodules and has been widely used in various applications. However, problemssuch as intra-category variability and inter-category similarity have beenholding back the models' performance, which leads to the need for new types offeatures to obtain a more meaningful scene representation. A semanticsegmentation mask provides pixel-level information about the objects availablein the scene, which makes it a promising source of information to obtain a moremeaningful local representation of the scene. Therefore, in this work, a novelapproach that uses a semantic segmentation mask to obtain a 2D spatial layoutof the object categories across the scene, designated by segmentation-basedsemantic features (SSFs), is proposed. These features represent, per objectcategory, the pixel count, as well as the 2D average position and respectivestandard deviation values. Moreover, a two-branch network, GS2F2App, thatexploits CNN-based global features extracted from RGB images and thesegmentation-based features extracted from the proposed SSFs, is also proposed.GS2F2App was evaluated in two indoor scene benchmark datasets: the SUN RGB-Dand the NYU Depth V2, achieving state-of-the-art results on both datasets.

A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification