A Volumetric Saliency Guided Image Summarization for RGB-D Indoor Scene
  Classification
Preeti Meena Himanshu Kumar Sandeep Yadav
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.