Texture classification and retrieval using random neural network model

Teke, Alper; Atalay, Volkan

doi:10.1109/iai.2004.1300955

Cited by 4 publications

(1 citation statement)

References 42 publications

(19 reference statements)

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“…The earlier work in [76] that uses the standard RNN for black and white texture generation utilizes similar principles. The RNN learning is also applied to image segmentation [113], vehicle classification [95], and texture classification and retrieval [141]. The work by Vlontzos [145] tested the RNN-based DL algorithms on two image recognition applications.…”

Section: Applications Of Random Neural Networkmentioning

confidence: 99%

Random Neural Network Methods and Deep Learning

Yin¹

2019

Prob. Eng. Inf. Sci.

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The random neural network (RNN) is a mathematical model for an “integrate and fire” spiking network that closely resembles the stochastic behavior of neurons in mammalian brains. Since its proposal in 1989, there have been numerous investigations into the RNN's applications and learning algorithms. Deep learning (DL) has achieved great success in machine learning. Recently, the properties of the RNN for DL have been investigated, in order to combine their power. Recent results demonstrate that the gap between RNNs and DL can be bridged and the DL tools based on the RNN are faster and can potentially be used with less energy expenditure than existing methods.

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Section: Applications Of Random Neural Networkmentioning

confidence: 99%