A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification
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Indoor scene classification has become an important task in perception modules and has been widely used in various applications. However, problems such as intra-category variability and inter-category similarity have been holding back the models' performance, which leads to the need for new types of features to obtain a more meaningful scene representation. A semantic segmentation mask provides pixel-level information about the objects available in the scene, which makes it a promising source of information to obtain a more meaningful local representation of the scene. Therefore, in this work, a novel approach that uses a semantic segmentation mask to obtain a 2D spatial layout of the object categories across the scene, designated by segmentation-based semantic features (SSFs), is proposed. These features represent, per object category, the pixel count, as well as the 2D average position and respective standard deviation values. Moreover, a two-branch network, GS2F2App, that exploits CNN-based global features extracted from RGB images and the segmentation-based features extracted from the proposed SSFs, is also proposed. GS2F2App was evaluated in two indoor scene benchmark datasets: the SUN RGB-D and the NYU Depth V2, achieving state-of-the-art results on both datasets.
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