Abstract
Up to now, rich and varied information, such as networks, multimedia information, especially images and visual information, has become an important part of information retrieval, in which video and image information has been an important basis. In recent years, an effective learning algorithm for standard feed-forward neural networks (FNNs), which can be used classifier and called random weights networks (RWN), has been extensively studied. This paper addresses the image classification algorithms using the algorithm. A new algorithm of image classification based on the RWN and principle component analysis (PCA) is proposed. The proposed algorithm includes significant improvements in classification rate, and the extensive experiments are performed using challenging databases. Compared with some traditional approaches, the new method has superior performances on both classification rate and running time.
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Cao, F., Zhao, J., Liu, B. (2013). Fast Image Classification Algorithms Based on Random Weights Networks. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_66
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DOI: https://doi.org/10.1007/978-3-642-39065-4_66
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