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PT-CNN: A Non-linear Lightweight Texture Image Classifier

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Abstract

Deep learning models provide better performance for image classification problems. The increased depth of the model, for enhancing the ability to represent features significantly raises the number of redundant parameters. These models require extensive memory usage and computational power that limits their ability to implement on platforms with constrained resources. This paper develops a novel lightweight deep learning architecture termed Parallel Texture Convolution Neural Network (PT-CNN) for multi-class texture classification. The proposed network is well suited to analyse texture images by learning and classifying complex texture features. The core building blocks of the network with convolution layer have a bank of filters that capture edge-like features from the input channels. These blocks act as a feature extractor that works in concurrent sessions to sought more complex features by lowering required training time. This block collaborates in lightweight processing of the proposed model. To analyse the performance of PT-CNN, experiments are conducted on the depth of building blocks that extract inherent texture attributes. The effectiveness of the suggested methodology is illustrated by experimenting on five texture datasets: Kth-tips2b, outex, kylberg sintorn rotation texture, alot and, kylberg rotation texture database. The proposed model outperforms on Adamax optimizer with high classification accuracy. Finally, the results from proposed lightweight model are compared with traditional methods, deep learning models and lightweight models.

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Acknowledgements

We thank the management of Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Tamil Nadu for the facilities to carry this work.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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  1. Department of Mathematics, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Tamil Nadu, India

    G. Sakthi Priya & N. Padmapriya

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  1. G. Sakthi Priya
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All author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, draft manuscript preparation also reviewed the results and approved the final version of the manuscript. All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.

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Priya, G.S., Padmapriya, N. PT-CNN: A Non-linear Lightweight Texture Image Classifier. Neural Process Lett 55, 8483–8507 (2023). https://doi.org/10.1007/s11063-023-11322-0

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