Jointly Class-specific and Shared Discriminative Dictionary Learning for Classifying Surface Defects of Steel Sheet

Zhou, Shiyang; Liu, Huaiguang; Cui, Ketao; Hao, Zhiqiang

doi:10.2355/isijinternational.isijint-2021-115

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…Discriminative sparse models have many applications in image classification, super-resolution [45], fault-diagnosis, etc. class-specific and shared discriminative dictionary learning (CASDDL) method [46] aims to classify the steel sheets based on the Fisher discrimination method.…”

Section: Literature Reviewmentioning

confidence: 99%

Toward real-time image annotation using marginalized coupled dictionary learning

Roostaiyan

Hosseini

Kashani

et al. 2022

J Real-Time Image Proc

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In most image retrieval systems, images include various high-level semantics, called tags or annotations. Virtually all the state-of-the-art image annotation methods that handle imbalanced labeling are search-based techniques which are time-consuming. In this paper, a novel coupled dictionary learning approach is proposed to learn a limited number of visual prototypes and their corresponding semantics simultaneously. This approach leads to a real-time image annotation procedure. Another contribution of this paper is that utilizes a marginalized loss function instead of the squared loss function that is inappropriate for image annotation with imbalanced labels. We have employed a marginalized loss function in our method to leverage a simple and effective method of prototype updating. Meanwhile, we have introduced 1 regularization on semantic prototypes to preserve the sparse and imbalanced nature of labels in learned semantic prototypes. Finally, comprehensive experimental results on various datasets demonstrate the efficiency of the pro-Authors contributed equally on this research.

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Section: Literature Reviewmentioning

confidence: 99%

Toward real-time image annotation using marginalized coupled dictionary learning

Roostaiyan

Hosseini

Kashani

et al. 2022

J Real-Time Image Proc

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show abstract

“…Ashour et al [38] 96.00 Unknown Zhou et al [45] 94.91 Unknown He et al [27] 99.56 Unknown Cohn et al [24] 99.40 Unknown Di et al [28] 96.50 Unknown Wan et al [26] 97.50 Unknown Liu et al [39] 98.89 5.58 Without pretraining 90.91 3.17…”

Section: Comparisons With State-of-the-artsmentioning

confidence: 99%

Surface Defect Classification of Steel Strip with Few Samples Based on Dual‐Stream Neural Network

Zhang

Yan

et al. 2021

steel research int.

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The automatic classification of surface defects is significant to the steel strip inspection system. However, influenced by the lack of samples, it is difficult to improve the classification accuracy. Herein, a novel dual‐stream neural network is proposed, which is composed by two streams of sample generation and classification training. Subsequently, numerous defect samples are generated for the classifier pretraining, and the real steel strip surface defects are classified by the transfer learning method. The experiment results demonstrate that the dual‐stream neural network has the highest classification accuracy of 99.70% among the state‐of‐the‐art classifiers, and the classification accuracy increasesd by 9.67% through generating plentiful similar images for pretraining. Moreover, the convolutional neural network (CNN) can improve the quality of generated defect images. When the number of epochs reaches 2000, the generated images are closest to the real labeled images. Finally, the cross‐validation proves that the dual‐stream neural network has excellent stability and robustness, without over‐fitting problem in the training process.

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Class-specific dictionary-based cross-domain scene classification method with reliable target pseudo labels for remote sensing images

Zhu,

Mo,

Zhuang

2024

Remote Sensing Applications: Society and Environment

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Jointly Class-specific and Shared Discriminative Dictionary Learning for Classifying Surface Defects of Steel Sheet

Cited by 4 publications

References 43 publications

Toward real-time image annotation using marginalized coupled dictionary learning

Toward real-time image annotation using marginalized coupled dictionary learning

Surface Defect Classification of Steel Strip with Few Samples Based on Dual‐Stream Neural Network

Class-specific dictionary-based cross-domain scene classification method with reliable target pseudo labels for remote sensing images

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