Abstract
As an important part of the national economy and an important supporting industry, printing and publishing industry is closely related to the development of national economy. In recent years, the massive publication and printing of books has made the work of storing books in databases more and more onerous. The maturity of deep learning technology has brought good news to recognition and classification of books. Convolutional neural network is a good tool. Convolutional neural network is a technology in deep learning, often used in computer vision, image recognition classification and other fields. Research results in the field of book recognition and classification are relatively lacking. There is no good book data set that can be used for neural network training. In this paper, we collected a large number of book data sets and we built a set of image classification models based on CNN to identify and classify the cover and back cover of books. Through a lot of training and testing, we have generated a set of CNN models that can effectively identify and classify the cover and back cover of books. Compared with the traditional way of manually entering books into database, the use of neural networks makes the work more efficient and saves a lot of human resources.
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References
Padfield, N.: Exploring the classification of acoustic transients with machine learning. In: Proceedings of ACOUSTICS 2019, vol. 10, no. 13 (2019)
Leng, J., Li, T., Bai, G., et al.: Cube-CNN-SVM: a novel hyperspectral image classification method. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1027–1034. IEEE (2016)
Yin, Q., Zhang, R., Shao, X.L.: CNN and RNN mixed model for image classification. In: MATEC Web of Conferences. EDP Sciences, vol. 277, p. 02001 (2019)
Zhang, M., Li, W., Du, Q.: Diverse region-based CNN for hyperspectral image classification. IEEE Trans. Image Process. 27(6), 2623–2634 (2018)
Qingling, J.: Edge detection for color image based on CNN. Int. J. Adv. Inf. Sci. Serv. Sci. 3(10), 61–69 (2011)
Jhang, K.: Gender prediction based on voting of CNN models. In: 2019 International Conference on Green and Human Information Technology (ICGHIT), pp. 89–92. IEEE (2019)
Piyush, R.: Hyperplane based classification: perceptron and (Intro to) support vector machines. In: CS5350/6350: Machine Learning (2011)
Domeniconi, C., Gunopulos, D., Peng, J.: Large margin nearest neighbor classifiers. IEEE Trans. Neural Netw. 16(4), 899–909 (2005)
Rokach, L., Maimon, O.: Data mining with decision trees: theory and applications. World Scientific Pub Co Inc. (2008). ISBN: 978-9812771711
Friedman, J.H.: Greedy function approximation: a gradient boosting machine (1999)
Breiman, L.: Statistical modeling: the two cultures. Stat. Sci. 16, 199–215 (2001)
Acknowlegments
1. Beijing science and technology innovation service capability construction project (PXM2016_014223_000025).
2. Major special project of science and technology of Guangdong Province, No: 190826175545233.
3. BIGC Project (Ec202007).
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Xia, H., Qi, Y., Zeng, Q., Li, Y., You, F. (2021). CNN-Based Book Cover and Back Cover Recognition and Classification. In: Weng, Y., Yin, Y., Kuang, L., Zhang, Z. (eds) Tools for Design, Implementation and Verification of Emerging Information Technologies. TridentCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 380. Springer, Cham. https://doi.org/10.1007/978-3-030-77428-8_5
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DOI: https://doi.org/10.1007/978-3-030-77428-8_5
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