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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 617618, 7 pages
Research Article

Recursive Neural Networks Based on PSO for Image Parsing

School of Sciences, Jimei University, Xiamen, China

Received 24 February 2013; Accepted 3 March 2013

Academic Editor: Zhenkun Huang

Copyright © 2013 Guo-Rong Cai and Shui-Li Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs). State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.