This work proposes a novel approach that uses a semantic segmentation mask to obtain a 2D spatial layout of the segmentation-categories across the scene, designated by segmentation-based semantic features (SSFs). These features represent, per segmentation-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.
@article{arxiv.2302.06432,
title = {A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification},
author = {Ricardo Pereira and Tiago Barros and Luis Garrote and Ana Lopes and Urbano J. Nunes},
journal= {arXiv preprint arXiv:2302.06432},
year = {2024}
}
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Published at Pattern Recognition Letters 2024 (DOI: 10.1016/j.patrec.2024.01.022)