mlsanityData to 7 Oct 2026

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

arXiv 2302.06432 ↗First posted 13 Feb 2023cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 13 Feb 2023.

Builds on

19 of 39 references.

References · 19

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7 more
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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classificationcited by 0