A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909088671072256 |
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| author | Pereira, Ricardo Barros, Tiago Garrote, Luis Lopes, Ana Nunes, Urbano J. |
| author_facet | Pereira, Ricardo Barros, Tiago Garrote, Luis Lopes, Ana Nunes, Urbano J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_06432 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification Pereira, Ricardo Barros, Tiago Garrote, Luis Lopes, Ana Nunes, Urbano J. Computer Vision and Pattern Recognition 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. |
| title | A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2302.06432 |