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2017 | OriginalPaper | Buchkapitel

Book Page Identification Using Convolutional Neural Networks Trained by Task-Unrelated Dataset

verfasst von : Leyuan Liu, Yi Zhao, Huabing Zhou, Jingying Chen

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

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Abstract

This paper presents a pipeline to make convolutional neural networks (CNNs) trained for another unrelated task available for book page identification. The pipeline has five building blocks: (1) An image segmentation module to separate book page from the background; (2) An image correction module to correct geometry and color distortions; (3) A feature extraction module to extract discriminative image features by a pre-trained CNN; (4) A feature compression module to reduce feature dimensions for speeding up; and (5) A feature matching module to calculate the similarity between a query image and a reference image, and then to find out the most similar reference image. The experimental results on a challenging testing dataset show that the proposed book page identification method achieves a top-5 hit rate of 98.93%.

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Vorheriges Kapitel Multi-instance Multi-label Learning for Image Categorization Based on Integrated Contextual Information

Nächstes Kapitel No Reference Assessment of Image Visibility for Dehazing

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Titel: Book Page Identification Using Convolutional Neural Networks Trained by Task-Unrelated Dataset
verfasst von: Leyuan Liu
Yi Zhao
Huabing Zhou
Jingying Chen
Verlag: Springer International Publishing
Buch: Image and Graphics
Print ISBN: 978-3-319-71606-0

Electronic ISBN: 978-3-319-71607-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-71607-7_57

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