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.
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Tian, L., Ma, YD., Liu, L., Zhan, K. (2013). Antinoise Texture Retrieval Based on PCNN and One-Class SVM. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_28
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DOI: https://doi.org/10.1007/978-3-642-38679-4_28
Publisher Name: Springer, Berlin, Heidelberg
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