Novel dual convolution adaptive focus neural network for book genre classification
Qingtao Zeng, Lixin Zhang, Jiefeng Zhao, Anping Xu, Yali Qi, Liqin Yu, Wenjing Li, Haochang Xia
TL;DR
This paper introduces a new neural network model for automatically classifying book genres using book cover images, improving classification accuracy.
Contribution
A novel dual-convolution adaptive focus neural network, CPPDE-YOLO, is proposed for enhanced book cover classification.
Findings
The CPPDE-YOLO model achieved a 1.1% improvement in Top_1 Accuracy over YOLOv8.
The model also improved Top_5 Accuracy by 1.0% on real datasets.
The hybrid convolution framework effectively captures complex features for better classification.
Abstract
Book covers typically contain a wealth of information. With the annual increase in the number of books published, deep learning has been utilised to achieve automatic identification and classification of book covers. This approach overcomes the inefficiency of traditional manual classification operations and enhances the management efficiency of modern book retrieval systems. In the realm of computer vision, the YOLO algorithm has garnered significant attention owing to its excellent performance across various visual tasks. Therefore, this study introduces the CPPDE-YOLO model, a novel dual-convolution adaptive focus neural network that integrates the PConv and PWConv operators, alongside dynamic sampling technology and efficient multi-scale attention. By incorporating specific enhancement features, the original YOLOv8 framework has been optimised to yield superior performance in book…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Text and Document Classification Technologies · Domain Adaptation and Few-Shot Learning