Learning two-pathway convolutional neural networks for categorizing scene images

• Published in: Multimedia tools and applications 2017-08, Vol.76 (15), p.16145-16162
• Main Authors: Bai, Shuang, Li, Zhaohong, Hou, Jianjun
• Format: Article
• Language: English
• Subjects: Categories; Classification; Computer architecture; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Landscape; Machine learning; Multimedia Information Systems; Neural networks; Redundancy; Selectivity; Special Purpose and Application-Based Systems
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-016-3900-6

Scenes are closely related to the kinds of objects that may appear in them. Objects are widely used as features for scene categorization. On the other hand, landscapes with more spatial structures of scenes are representative of scene categories. In this paper, we propose a deep learning based algorithm for scene categorization. Specifically, we design two-pathway convolutional neural networks for exploiting both object attributes and spatial structures of scene images. Different from conventional deep learning methods, which usually focus on only one aspect of images, each pathway of the proposed architecture is tuned to capture a different aspect of images. As a result, complementary information of image contents can be utilized effectively. In addition, to deal with the feature redundancy problem caused by combining features from different sources, we adopt the ℓ 2,1 norm during classifier training to control selectivity of each type of features. Extensive experiments are conducted to evaluate the proposed method. Obtained results demonstrate that the proposed approach achieves superior performances over conventional methods. Moreover, the proposed method is a general framework, which can be easily extended to more pathways and applied to solve other problems.