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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Compact Image Representation Model Based on Both nCRF and Reverse Control Mechanisms
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Compact Image Representation Model Based on Both nCRF and Reverse Control Mechanisms

机译：基于nCRF和反向控制机制的紧凑图像表示模型

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The aim of this paper is to construct a bio-inspired hierarchical neural network that could accurately represent visual images and facilitate follow-up processing. Our computational model adopted a ganglion cell (GC) mechanism with a receptive field that dynamically self-adjusts according to the characteristics of an input image. For each GC, a micro neural circuit and a reverse control circuit were developed to self-adaptively resize the receptive field. An array was also designed to imitate the layer of GCs that perform image representation. Results revealed that this GC array could represent images from the external environment with a low processing cost, and this nonclassical receptive field mechanism could substantially improve both segmentation and integration processing. This model enables automatic extraction of blocks from images, which makes multiscale representation feasible. Importantly, once an original pixel-level image was reorganized into a GC array, semantic-level features emerged. Because GCs, like symbols, are discrete and separable, this GC-grained compact representation is open to operations that can manipulate images partially and selectively. Thus, the GC-array model provides a basic infrastructure and allows for high-level image processing.
机译：本文的目的是构建一个生物启发的层次神经网络，该网络可以准确地表示视觉图像并促进后续处理。我们的计算模型采用了神经节细胞（GC）机制，该神经节细胞具有根据输入图像的特征动态自我调整的感受野。对于每个GC，都开发了微神经电路和反向控制电路以自适应地调整接收场的大小。还设计了一个阵列来模仿执行图像表示的GC层。结果表明，该GC阵列可以以较低的处理成本表示来自外部环境的图像，并且这种非经典的接收场机制可以显着改善分割和集成处理。该模型能够从图像中自动提取块，从而使多尺度表示成为可能。重要的是，一旦将原始像素级图像重组为GC数组，就会出现语义级功能。由于GC与符号一样是离散且可分离的，因此这种具有GC粒度的紧凑表示形式对可以部分地和选择性地操作图像的操作开放。因此，GC阵列模型提供了基本的基础结构，并允许进行高级图像处理。

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