An experimental evaluation of echo state network for colour image segmentation

Souahlia, Abdelkerim, Belatreche, Ammar, Benyettou, Abdelkader and Curran, Kevin (2016) An experimental evaluation of echo state network for colour image segmentation. In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, Piscataway, pp. 1143-1150. ISBN 978-1-5090-0621-2

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Image segmentation refers to the process of dividing an image into multiple regions which represent meaningful areas. Image segmentation is an essential step for most image analysis tasks such as object recognition and tracking, pattern recognition, content-based image retrieval, etc. In recent years, a large number of image segmentation algorithms have been developed, but achieving accurate segmentation still remains a challenging task. Recently, reservoir computing (RC) has drawn much attention in machine learning as a new model of recurrent neural networks (RNN). Echo State Network (ESN) represents one efficient realization of RC, which is initially designed to facilitate learning in Recurrent Neural Networks. In this paper we investigate the viability of ESN as feature extractor for pixel classification based colour image segmentation. Extensive experiments are conducted on real world colour image datasets and the global ESN reservoir parameters are varied to identify their operating ranges that allow the use of the reservoir nodes internal activations as new pixel features for the colour image segmentation task. A simple feed forward neural network is used to realize the ESN readout function and classify these new features. The experimental results show that the proposed method achieves high performance image segmentation comparing with state-of-the-art techniques. In addition, a set of empirically derived guidelines for setting the reservoir global parameters are proposed.

Item Type: Book Section
Uncontrolled Keywords: feature extraction, echo state network, colour image segmentation, pixel classification
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 25 Jan 2017 14:59
Last Modified: 12 Oct 2019 22:27

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