A Volumetric Saliency Guided Image Summarization for RGB-D Indoor Scene Classification
This work addresses scene identification in RGB-D indoor environments, but it appears incremental as it builds on existing saliency methods by incorporating volumetric features.
The paper tackled the problem of generating image summaries for indoor scene classification by proposing a volumetric saliency-guided framework, which improved scene identification by focusing on stationary characteristics rather than foreground objects, though no concrete numbers were provided to quantify the results.
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.