An image classification using convolutional sparse representation and cone-restricted subspace method
An image classification using convolutional sparse representation and cone-restricted subspace method
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DOI：
10.1109/iciibms52876.2021.9651582
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Yosuke Higuchi-;Tomoya Hirakawa;Y. Kuroki
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文献类型：
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作者：
Yosuke Higuchi-;Tomoya Hirakawa;Y. Kuroki
提出了一种基于卷积稀疏表示(CSR)和锥限定子空间方法(CRSM)的分类方法。CSR提取图像特征作为卷积滤波器，并将特征映射显示为滤波器的系数。该方案类似于卷积神经网络(CNN)的卷积层。为了增加对图像漂移的鲁棒性，本文使用了功率谱作为一种类似于CNN上的激活函数的非线性算子。由于功率谱的非负性，CRSM将每组类别近似为一个锥体；此外，非负矩阵分解(NMF)生成CRSM的基向量。在手写图像分类上的实验表明，当学习图像的数目小于1,000幅时，本文提出的三层网络结构的识别率比相同结构的CNN方法更高。
This paper presents a classification method based on Convolutional Sparse Representation (CSR) and Cone Restricted Subspace Method (CRSM). CSR extracts image features as convolutional filters and shows the feature maps as coefficients of the filters. This scheme is similar to a convolutional layer of CNNs (Convolutional Neural Networks). To increase the robustness against image shift, this work uses power spectrum as a non-linear operator like activate functions on CNNs. CRSM approximates each set of class as a cone because of non-negativeness of the power spectrum; furthermore, Nonnegative Matrix Factorization (NMF) generates the basis vectors of CRSM. Experiments on handwritten image classification shows that our method using a 3-layer network shows higher recognition ratio than the same structure of CNN when the number of learning images is less than 1,000.