一种新的改进的二值卷积图像分类模型

Pub Date : 2022-12-23 DOI:10.12694/scpe.v23i4.2029
P. Hemalatha, G. Shankar, D. M. Deepak Raj
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引用次数: 0

摘要

在深度学习中开发了许多图像分类策略。然而,由于图像本身的复杂性,传统的图像分类策略已经不能满足实际应用的需要。随着像素信息量的增加,分类难度加大。然而,CNN因其简单、准确而被广泛应用于图像中的目标识别方法,但是,哪种策略对分析和识别图像中的目标最有利,目前还不清楚。在本文中,我们引入了一种CNN网络和基于聚类的IBCNN技术来进行基于patch提取的分类。本文提出的方法可以通过以下四种不同的方式实现它们的目标:a)自动核选择;B)弹性斑块大小选择;c) CNN层;d)池化层修改。此外,还对池化层进行平均值修改,并计算像素大小。将该方法应用于10个不同的图像数据集。最后,将提出的模型与WCNN、MLP和ELM-CNN等三种基准模型进行比较,以估计其性能。实验结果表明,该方法与其他模型相比具有较强的竞争力。
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A New Improved Binary Convolutional Model for Classification of Images
There are numerous image classification strategies are developed in deep learning. However, due to the complexity of images, conventional image classification strategies have been incapable to meet real application needs. As the amount of pixel information rises, the classification becomes more difficult. However, CNN is widely used method for object identification in picture due to its simple and accurate, but still, it remains hazy which strategies are most supportive for analysing and distinguishing the objects in pictures. In this paper we introduced a CNN network and clustering-based technique called IBCNN to perform classification based on patch extraction. The proposed method can accomplish their goals in the following four different ways: a) Automatic Kernel selection; b) resilient patch size selection; c) CNN layer; and d) pooling layer modification. In addition, it also modifies the pooling layer with average value and calculate the pixel size. The proposed method was applied on ten different image datasets. Finally, the proposed model is compared to three benchmarking models: such as WCNN, MLP, and ELM-CNN to estimate its performance. The obtained results shows that the proposed method gives competitive results compared to the other models.
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