.png){kind=icon}
返回
Shuffled-Xception-DarkNet-53: A content-based image retrieval model based on deep learning algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2023.108647.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes Shuffled-Xception-DarkNet-53, an advanced version of DarkNet-53 for Content-Based Image Retrieval (CBIR). The proposed model introduced the notion of the Shuffled -Xception module, which uses three sets of 1 x 1, 3 x 3, and 5 x 5 size filters using a serial connection in place of a single 3 x 3 size filter to extract more refined features from the input images. Instead of the standard 2D Convolution operation, 'Group Convolution' is employed in the proposed Shuffled -Xception module to make the training process of the proposed CNN more efficient. Furthermore, 'Group Convolution' improves the co-relations among the filters of the corresponding Shuffled -Xception module, resulting in more informative features. Between every two serial Group Convolution layers of the same size, one Channel Shuffle layer is used to prevent the loss of information flow among the channels of different groups. The proposed method outperformed the twelve compared methods, including conventional and CNN-based CBIR methods, in ten standard image datasets.
Keyword:
CBIR
Deep learning
Xception concept
Channel shuffle
DarkNet-53
Group convolution
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
引用论文
分享收藏Efficient content-based image retrieval using deep search and rescue algorithm.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏
查看更多内容