Convolutional Neural Network based Classification for automatic segregation of distorted digital images

Saurabh, Nikitha; Prathyakshini,; Salian, K Preethi

doi:10.1109/iccsea54677.2022.9936379

2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA) 2022

DOI: 10.1109/iccsea54677.2022.9936379

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Convolutional Neural Network based Classification for automatic segregation of distorted digital images

Nikitha Saurabh¹,

Prathyakshini Prathyakshini²,

K Preethi Salian³

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2024

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Cited by 1 publication

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Employing Different Algorithms of Lightweight Convolutional Neural Network Models in Image Distortion Classification

Ahmed,

Abdulazeez,

Hammad

2024

Computers

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The majority of applications use automatic image recognition technologies to carry out a range of tasks. Therefore, it is crucial to identify and classify image distortions to improve image quality. Despite efforts in this area, there are still many challenges in accurately and reliably classifying distorted images. In this paper, we offer a comprehensive analysis of models of both non-lightweight and lightweight deep convolutional neural networks (CNNs) for the classification of distorted images. Subsequently, an effective method is proposed to enhance the overall performance of distortion image classification. This method involves selecting features from the pretrained models’ capabilities and using a strong classifier. The experiments utilized the kadid10k dataset to assess the effectiveness of the results. The K-nearest neighbor (KNN) classifier showed better performance than the naïve classifier in terms of accuracy, precision, error rate, recall and F1 score. Additionally, SqueezeNet outperformed other deep CNN models, both lightweight and non-lightweight, across every evaluation metric. The experimental results demonstrate that combining SqueezeNet with KNN can effectively and accurately classify distorted images into the correct categories. The proposed SqueezeNet-KNN method achieved an accuracy rate of 89%. As detailed in the results section, the proposed method outperforms state-of-the-art methods in accuracy, precision, error, recall, and F1 score measures.

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Employing Different Algorithms of Lightweight Convolutional Neural Network Models in Image Distortion Classification

Ahmed,

Abdulazeez,

Hammad

2024

Computers

View full text Add to dashboard Cite

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Convolutional Neural Network based Classification for automatic segregation of distorted digital images

Cited by 1 publication

References 21 publications

Employing Different Algorithms of Lightweight Convolutional Neural Network Models in Image Distortion Classification

Employing Different Algorithms of Lightweight Convolutional Neural Network Models in Image Distortion Classification

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