Abstract and Applied Analysis

Recursive Neural Networks Based on PSO for Image Parsing

Guo-Rong Cai and Shui-Li Chen

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Abstract

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.

Article information

Source
Abstr. Appl. Anal., Volume 2013, Special Issue (2013), Article ID 617618, 7 pages.

Dates
First available in Project Euclid: 26 February 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1393450359

Digital Object Identifier
doi:10.1155/2013/617618

Mathematical Reviews number (MathSciNet)
MR3039184

Citation

Cai, Guo-Rong; Chen, Shui-Li. Recursive Neural Networks Based on PSO for Image Parsing. Abstr. Appl. Anal. 2013, Special Issue (2013), Article ID 617618, 7 pages. doi:10.1155/2013/617618. https://projecteuclid.org/euclid.aaa/1393450359


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