Title :
Quaddirectional 2D-Recurrent Neural Networks For Image Labeling
Author :
Bing Shuai ; Zhen Zuo ; Gang Wang
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
Abstract :
We adopt Convolutional Neural Networks (CNN) to learn discriminative features for local patch classification. We further introduce quaddirectional 2D Recurrent Neural Networks to model the long range dependencies among pixels. Our quaddirectional 2D-RNN is able to embed the global image context into the compact local representation, which significantly enhance their discriminative power. Our experiments demonstrate that the integration of CNN and quaddirectional 2D-RNN achieves very promising results which are comparable to state-of-the-art on real-world image labeling benchmarks.
Keywords :
feature extraction; image classification; learning (artificial intelligence); recurrent neural nets; CNN; compact local representation; convolutional neural networks; discriminative feature learning; discriminative power enhancement; global image context; image labeling; image pixels; local patch classification; long-range dependency model; quaddirectional 2D-RNN; quaddirectional 2D-recurrent neural networks; real-world image labeling benchmarks; Context; Convolution; Feature extraction; Hidden Markov models; Labeling; Recurrent neural networks; Convolutional neural networks; image labeling; quaddirectional 2D-RNN; recurrent neural networks;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2015.2441781
