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首页> 外文会议>International conference on graphic and image processing >A New Pattern Associative Memory Model for Image Recognition Based on Hebb Rules and Dot Product
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A New Pattern Associative Memory Model for Image Recognition Based on Hebb Rules and Dot Product

机译：基于Hebb规则和点积的图像识别新模式关联记忆模型

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摘要

A great number of associative memory models have been proposed to realize information storage and retrieval inspired by human brain in the last few years. However, there is still much room for improvement for those models. In this paper, we extend a binary pattern associative memory model to accomplish real-world image recognition. The learning process is based on the fundamental Hebb rules and the retrieval is implemented by a normalized dot product operation. Our proposed model can not only fulfill rapid memory storage and retrieval for visual information but also have the ability on incremental learning without destroying the previous learned information. Experimental results demonstrate that our model outperforms the existing Self-Organizing Incremental Neural Network (SOINN) and Back Propagation Neuron Network (BPNN) on recognition accuracy and time efficiency.
机译：在过去的几年中，已经提出了大量的联想记忆模型来实现人脑启发下的信息存储和检索。但是，这些模型仍有很大的改进空间。在本文中，我们扩展了二进制模式关联记忆模型，以完成现实世界中的图像识别。学习过程基于基本的Hebb规则，而检索则通过归一化的点积运算来实现。我们提出的模型不仅可以实现视觉信息的快速存储和检索，还可以在不破坏先前学习到的信息的情况下进行增量学习。实验结果表明，我们的模型在识别准确性和时间效率方面优于现有的自组织增量神经网络（SOINN）和反向传播神经元网络（BPNN）。

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