Labeled projective dictionary pair learning: application to handwritten numbers recognition
Labeled projective dictionary pair learning: application to handwritten numbers recognition
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DOI：
10.1016/j.ins.2022.07.070
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发表时间：
2022-07-26
影响因子：
8.1
通讯作者：
Abolghasemi, Vahid
中科院分区：
文献类型：
--
作者：
Ameri, Rasoul;Alameer, Ali;Abolghasemi, Vahid
字典学习被引入稀疏图像表示。今天，它是图像分类的基石。我们提出了一种新的字典学习方法来识别手写数字图像。我们的重点是最大限度地提高稀疏表示和歧视的权力，类特定的字典。我们首次采用了一个新的特征空间，即，方向梯度直方图（HOG），以生成字典列（原子）。HOG特征鲁棒地描述了手写体的精细细节。我们设计了一个目标函数，然后通过最小化技术，同时将这些功能。建议的成本函数的好处，从一个新的类标签惩罚项约束相关的最小化方法，以获得类特定的字典。将该方法应用于三种不同语言的各种手写图像数据库的结果显示，与其他相关方法相比，该方法的分类性能提高了98%。此外，我们表明，HOG特征与字典学习相结合，与使用原始数据时相比，准确率提高了11%。最后，我们证明了我们提出的方法在相同的实验条件下，但使用一小部分参数，可以实现与现有深度学习模型相当的结果。(c)2022作者。由爱思唯尔公司出版这是CC下的开放获取文章
Dictionary learning was introduced for sparse image representation. Today, it is a cornerstone of image classification. We propose a novel dictionary learning method to recognise images of handwritten numbers. Our focus is to maximise the sparse-representation and discrimination power of the class-specific dictionaries. We, for the first time, adopt a new feature space, i.e., histogram of oriented gradients (HOG), to generate dictionary columns (atoms). The HOG features robustly describe fine details of hand-writings. We design an objective function followed by a minimisation technique to simultaneously incorporate these features. The proposed cost function benefits from a novel class-label penalty term constraining the associated minimisation approach to obtain class-specific dictionaries. The results of applying the proposed method on various handwritten image databases in three different languages show enhanced classification performance (- 98%) compared to other relevant methods. Moreover, we show that combination of HOG features with dictionary learning enhances the accuracy by 11% compared to when raw data are used. Finally, we demonstrate that our proposed approach achieves comparable results to that of existing deep learning models under the same experimental conditions but with a fraction of parameters. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC