Scene recognition is challenging due to the intra-class diversity and inter-class similarity.
Previous works recognize scenes either with global representations or with the intermediate …

Multi-modal fusion has been proved to help enhance the performance of scene classification
tasks. This paper presents a 2D–3D Fusion stage that combines 3D Geometric Features with …

RGB-D indoor scene classification is an essential and challenging task. Although
convolutional neural network (CNN) achieves excellent results on RGB-D object recognition …

Spectral signatures of natural scenes were earlier found to be distinctive for different scene
types with varying spatial envelope properties such as openness, naturalness, ruggedness …

This paper focuses on the task of RGB indoor scene classification. A single scene may
contain various configurations and points of view, but there are a small number of objects …

A scene is usually abstract that consists of several less abstract entities such as objects or
themes. It is very difficult to reason scenes from visual features due to the semantic gap …

Indoor scenes are usually characterized by scattered objects and their relationships, which
turns the indoor scene classification task into a challenging computer vision task. Despite …

Geometric 3D scene classification is a very challenging task. Current methodologies extract
the geometric information using only a depth channel provided by an RGB-D sensor. These …

Due to the abstraction of scenes, comprehensive scene understanding requires semantic
modeling in both global and local aspects. Scene recognition is usually researched from a …

Scene recognition is an important branch of computer vision and a common task for deep
learning. As is known to all, different scenes are supported by different “key objects” …