# A Volumetric Saliency Guided Image Summarization for RGB-D Indoor Scene Classification

arXiv: https://arxiv.org/abs/2401.16227 · HTML: https://mlsanity.com/paper/2401.16227/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 97 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 21 of 21)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding](https://mlsanity.com/paper/2011.02523/) (2020)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks](https://mlsanity.com/paper/1907.06781/) (2019)
- [Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2007.06227/) (2020)
- [Siamese Network for RGB-D Salient Object Detection and Beyond](https://mlsanity.com/paper/2008.12134/) (2020)
- [SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection](https://mlsanity.com/paper/2204.05585/) (2022)
- [Uncertainty Inspired RGB-D Saliency Detection](https://mlsanity.com/paper/2009.03075/) (2020)
- [RGB-D Salient Object Detection with Cross-Modality Modulation and Selection](https://mlsanity.com/paper/2007.07051/) (2020)
- [Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection](https://mlsanity.com/paper/2010.05537/) (2020)
- [RGB-D Salient Object Detection via 3D Convolutional Neural Networks](https://mlsanity.com/paper/2101.10241/) (2021)
- [Bilateral Attention Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2004.14582/) (2020)
- [HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness](https://mlsanity.com/paper/2301.07405/) (2023)
- [HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection](https://mlsanity.com/paper/2301.03036/) (2023)
- [Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection](https://mlsanity.com/paper/2306.03630/) (2023)
- [Motif Mining: Finding and Summarizing Remixed Image Content](https://mlsanity.com/paper/2203.08327/) (2022)
- [Saliency Detection via Global Context Enhanced Feature Fusion and Edge Weighted Loss](https://mlsanity.com/paper/2110.06550/) (2021)
- [Minimum message length estimation of mixtures of multivariate Gaussian and von Mises-Fisher distributions](https://mlsanity.com/paper/1502.07813/) (2015)
- [Exploiting Color Name Space for Salient Object Detection](https://mlsanity.com/paper/1703.08912/) (2017)

## Built on by (most cited first, 1 of 1)

- [Scene Representation using 360deg Saliency Graph and its Application in Vision-based Indoor Navigation](https://mlsanity.com/paper/2603.20353/) (2026)

## Abstract (arXiv)

Image summary, an abridged version of the original visual content, can be used to represent the scene. Thus, tasks such as scene classification, identification, indexing, etc., can be performed efficiently using the unique summary. Saliency is the most commonly used technique for generating the relevant image summary. However, the definition of saliency is subjective in nature and depends upon the application. Existing saliency detection methods using RGB-D data mainly focus on color, texture, and depth features. Consequently, the generated summary contains either foreground objects or non-stationary objects. However, applications such as scene identification require stationary characteristics of the scene, unlike state-of-the-art methods. This paper proposes a novel volumetric saliency-guided framework for indoor scene classification. The results highlight the efficacy of the proposed method.