Antinoise texture retrieval based on PCNN and one-class SVM

Authors:
Le Tian;Yi-De Ma;Li Liu;Kun Zhan
Affiliations:
School of Information Science and Engineering, Lanzhou University, Lanzhou, China;School of Information Science and Engineering, Lanzhou University, Lanzhou, China;School of Information Science and Engineering, Lanzhou University, Lanzhou, China;School of Information Science and Engineering, Lanzhou University, Lanzhou, China
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Year:
2013

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Abstract

By training and predicting the features that are extracted by pulse coupled neural network (PCNN), a noise immunity texture retrieval system combined with PCNN and one-class support vector machine (OCSVM) is proposed in this paper, which effectively improve the anti-noise performance of image retrieval system. The experiment results in different noise environment show that our proposed algorithm is able to obtain higher retrieval accuracy and better robustness to noise than traditional Euclidean distance based system.