Image Recommendation Algorithm Based on Deep Learning
With the rapid development of network technology, the number of digital images is growing at an alarming rate, people's demand for information gradually shift from text into images. However, it is very difficult for users to quickly find the images they are interested in from the large number o...
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doaj-18d395154f5a4356bcd4175c06a398fb2021-03-30T04:42:54ZengIEEEIEEE Access2169-35362020-01-01813279913280710.1109/ACCESS.2020.30073539138411Image Recommendation Algorithm Based on Deep LearningPei Yin0Liang Zhang1https://orcid.org/0000-0002-3840-185XBusiness School, University of Shanghai for Science and Technology, Shanghai, ChinaSchool of Aerospace Engineering, Tsinghua University, Beijing, ChinaWith the rapid development of network technology, the number of digital images is growing at an alarming rate, people's demand for information gradually shift from text into images. However, it is very difficult for users to quickly find the images they are interested in from the large number of image libraries. The purpose of this paper is to study the image recommendation algorithm based on deep learning. In this paper, image classification algorithm is firstly studied. LReLU - Softplus activation function is formed by combining LReLU function and Softplus function, and CNN is improved. Then, an image retrieval model based on local sensitive hash algorithm is proposed in this paper. This model calculates the distance in hamming space for the binary hash code generated by mapping. Euclidean distance is calculated inside the result set after similarity measurement to improve the accuracy, and the image retrieval model is constructed. Finally, an image recommendation model based on implicit support vector machine (SVM) is proposed in this paper. This image recommendation method combines image text information and image content information. The experimental results show that the proposed image recommendation model can meet the practical needs. In this paper, the overlap rate between the CNN-based recommendation model and the human recommendation algorithm was tested, and the coincidence degree of the two recommended images reached 88%.https://ieeexplore.ieee.org/document/9138411/Deep Learningconvolutional neural networkimage retrieval algorithmimage recommendation algorithmimplicit support vector machine |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pei Yin Liang Zhang |
spellingShingle |
Pei Yin Liang Zhang Image Recommendation Algorithm Based on Deep Learning IEEE Access Deep Learning convolutional neural network image retrieval algorithm image recommendation algorithm implicit support vector machine |
author_facet |
Pei Yin Liang Zhang |
author_sort |
Pei Yin |
title |
Image Recommendation Algorithm Based on Deep Learning |
title_short |
Image Recommendation Algorithm Based on Deep Learning |
title_full |
Image Recommendation Algorithm Based on Deep Learning |
title_fullStr |
Image Recommendation Algorithm Based on Deep Learning |
title_full_unstemmed |
Image Recommendation Algorithm Based on Deep Learning |
title_sort |
image recommendation algorithm based on deep learning |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
With the rapid development of network technology, the number of digital images is growing at an alarming rate, people's demand for information gradually shift from text into images. However, it is very difficult for users to quickly find the images they are interested in from the large number of image libraries. The purpose of this paper is to study the image recommendation algorithm based on deep learning. In this paper, image classification algorithm is firstly studied. LReLU - Softplus activation function is formed by combining LReLU function and Softplus function, and CNN is improved. Then, an image retrieval model based on local sensitive hash algorithm is proposed in this paper. This model calculates the distance in hamming space for the binary hash code generated by mapping. Euclidean distance is calculated inside the result set after similarity measurement to improve the accuracy, and the image retrieval model is constructed. Finally, an image recommendation model based on implicit support vector machine (SVM) is proposed in this paper. This image recommendation method combines image text information and image content information. The experimental results show that the proposed image recommendation model can meet the practical needs. In this paper, the overlap rate between the CNN-based recommendation model and the human recommendation algorithm was tested, and the coincidence degree of the two recommended images reached 88%. |
topic |
Deep Learning convolutional neural network image retrieval algorithm image recommendation algorithm implicit support vector machine |
url |
https://ieeexplore.ieee.org/document/9138411/ |
work_keys_str_mv |
AT peiyin imagerecommendationalgorithmbasedondeeplearning AT liangzhang imagerecommendationalgorithmbasedondeeplearning |
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1724181296330047488 |