.png){kind=icon}
.png){kind=icon}
Return
Semantic-aware visual scene representation
.png){kind=icon}
PRE
AI
DOI：10.1007/s13735-022-00246-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Scene classification is a mature and active computer vision task, due to the inherent ambiguity. The scene classification task aims to classify the visual scene images in predefined categories based on the ambient content, objects and the layout of the images. Inspired by human visual scene understanding, the visual scenes can be divided into two categories: (1) Object-based scenes that consist of the scene-specific objects and can be understood with those objects. (2) Layout-based scenes that are understandable based on the layout and the ambient content of the scene images. Scene-specific objects semantically help to understand object-based scenes, whereas the layout and the ambient content are effective in understanding layout-based scenes by representing the visual appearance of the scene images. Hence, one of the main challenges in scene classification is to create a discriminative representation that can provide a high-level perception of visual scenes. Accordingly, we have presented a discriminative hybrid representation of visual scenes, in which semantic features extracted from scene-specific objects are fused with visual features extracted from a deep CNN. The proposed scene representation method is used for the scene classification task and is applied to three benchmark scene datasets including: MIT67, SUN397, and UIUC Sports. Moreover, a new scene dataset, called Scene40, has been introduced, and also, the results of our proposed method have been presented on it. Experimental results show that our proposed method has achieved remarkable performance in the scene classification task and has outperformed the state-of-the-art methods.
Keywords:
Scene representation
Scene-specific object
Semantic feature
Deep visual feature
Journal
IF:
2.9
Papers:
270
Citations:
866