# A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification Source: [A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:17:23.201Z Authors: Ricardo Pereira, Tiago Barros, Luis Garrote, Ana Lopes, Urbano J. Nunes Published: 2023-02-13T15:12:11.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()