Abstract
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.
Abstract (translated)
图像摘要，是对原始视觉内容的一个简要概述，可以用来表示场景。因此，场景分类、识别、索引等任务可以使用独特的摘要来高效执行。最常见的生成相关图像摘要的技术是显著性。然而，显著性的定义在本质上是有主观性的，并取决于应用场景。使用RGB-D数据现有的 saliency 检测方法主要关注颜色、纹理和深度特征。因此，生成的摘要包含前景物体或非稳定物体。然而，场景识别应用程序需要场景的静止特性，而与现有方法不同。本文提出了一种新颖的体积显著性引导的室内场景分类框架。结果突出了所提出方法的有效性。
URL
https://arxiv.org/abs/2401.16227
