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受传统局部二进制模式（Local binary pattern，LBP）的启发，本文提出了局部二进制卷积（LBC）层，作为标准 CNN 卷积层的替代方案。 本文提出了一种创新的二值化池化 (Binary Pooling)方法，通过自适应阈值二值化局部差异来增强CNN对细粒度纹理特征的捕捉能力。 本文提出局部二值卷积 (LBC)层，作为标准CNN中卷积层的替代方案。 LBC层由固定稀疏二值滤波器、非线性激活函数和可学习线性权重组成，能显著节省参数和模型大小。
本文主要内容：把局部二值与卷积神经网路结合，以削减参数，从而实现深度卷积神经网络端到端的训练，也就是未来嵌入式设备上跑卷积效果将会越来越好。 本文聚焦卷积神经网络中的局部卷积，重点解析全连接、局部连接、全卷积与局部卷积四种连接方式的差异，结合人脸识别任务案例，阐述局部卷积的应用场景及优势，为理解卷积网络连接机制提供技术参考。 随着深度学习的兴起，LBP方法也开始与卷积神经网络（CNN）等深度学习模型相结合。 例如，有研究者提出了基于LBP的卷积层，用于在CNN中嵌入局部纹理信息。 这种融合传统纹理描述算子和深度学习模型的方法为计算机视觉领域带来了新的研究方向和可能性。
局部二值模式LBP（Local Binary Patterns）是一种简单但有效的空间特征描述算子，能够减轻CNN特征提取的压力并提高分类精度。
为此，本文提出一种结合局部二值模式 (Local binary pattern, LBP)和卷积神经网络的人脸美丽预测算法。 Our proposed method reduces information loss since it considers the neighborhood and size of the pixels in the pooling region. 该文主要贡献是将局部二值（Local binary patterns, LBP）与卷积神经网络 (Convolution nueral networks, CNN)结合,削减了网络参数，希望实现将基于卷积神经网络（CNN）的图像处理技术（如人脸识别等）应用在嵌入式设备上，提高识别效率。
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