Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation

• Published in: IEEE transactions on circuits and systems for video technology Vol. 27; no. 6; pp. 1263 - 1274
• Main Authors: Xie, Guo-Sen, Zhang, Xu-Yao, Yan, Shuicheng, Liu, Cheng-Lin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Clustering; Construction; Convolutional codes; Convolutional neural networks (CNNs); Dictionaries; dictionary; domain adaptation (DA); Fisher vector; Matching; Neural networks; Object oriented modeling; part learning; Performance enhancement; Recognition; Representations; Scale (ratio); scene recognition; Spectra; State of the art; Training; Visual tasks; Visualization

Convolutional neural network (CNN) has achieved the state-of-the-art performance in many different visual tasks. Learned from a large-scale training data set, CNN features are much more discriminative and accurate than the handcrafted features. Moreover, CNN features are also transferable among diff...