| Title | An Improved Deep Pairwise Supervised Hashing Algorithm for Fast Image Retrieval |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Yan, Longchuan, Zhang, Zhaoxia, Huang, Huige, Yuan, Xiaoyu, Peng, Yuanlong, Zhang, Qingyun |
| Conference Name | 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) |
| Keywords | compositionality, deep hashing algorithm, deep neural networks, feature extraction, hash algorithms, hash code learning, Image coding, image retrieval, learning (artificial intelligence), Manuals, pubcrawl, Quantization (signal), representation learning, resilience, Resiliency |
| Abstract | In recent years, hashing algorithm has been widely researched and has made considerable progress in large-scale image retrieval tasks due to its advantages of convenient storage and fast calculation efficiency. Nowadays most researchers use deep convolutional neural networks (CNNs) to perform feature learning and hash coding learning at the same time for image retrieval and the deep hashing methods based on deep CNNs perform much better than the traditional manual feature hashing methods. But most methods are designed to handle simple binary similarity and decrease quantization error, ignoring that the features of similar images and hashing codes generated are not compact enough. In order to enhance the performance of CNNs-based hashing algorithms for large scale image retrieval, this paper proposes a new deep-supervised hashing algorithm in which a novel channel attention mechanism is added and the loss function is elaborately redesigned to generate compact binary codes. It experimentally proves that, compared with the existing hashing methods, this method has better performance on two large scale image datasets CIFAR-10 and NUS-WIDE. |
| DOI | 10.1109/ICIBA52610.2021.9688315 |
| Citation Key | yan_improved_2021 |
An Improved Deep Pairwise Supervised Hashing Algorithm for Fast Image Retrieval