| Infrared and Visible Cross-Modal Image Retrieval through Shared Features | |||
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Fangcen Liu1,2,
Chenqiang Gao1,2,
Yongqing Sun1,2,
Yue Zhao1,2, Feng Yang1,2, Anyong Qin1,2, Deyu Meng3,4 |
1School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, 2 Chongqing Key Laboratory of Signal and Information Processing, 3Macau Institute of Systems Engineering, Macau University of Science and Technology, 4School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi’an Jiaotong University, |
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| Abstract | |||
| Image retrieval is one of the key techniques of computer vision, and has been studied for a long time. Nevertheless, little attention is paid to infrared and visible cross-modal retrieval which can be widely used in various applications, e.g., infrared and visible surveillance systems. In this paper, we propose a shared features based infrared-visible cross-modal image retrieval method. The similar visual features are extracted from infrared and visible images as the shared features, and the Euclidean distance is used to measure the similarity between these features. The core of the proposed method comes from three aspects: 1) Feature separation network can separate image features into shared features and exclusive features; 2) Maximum Mean Discrepancy (MMD) loss is employed to constrain the distribution of shared features, which can reduce the retrieval error caused by different imaging angles and similarity of infrared images. 3) The cross-layer fusion encoder compensates for the context loss in the convolution of infrared images. Experimental results on the Infrared-Visible dataset demonstrate the proposed method is effective and outperforms the state-of-the-art approaches. | |||
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| Network Overview | |||
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| Fig.1 The proposed framework of infrared and visible image retrieval in this paper. | |||
| Results | Result Gallery | ||
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| Tab.1 Performance comparisons of different methods. | |||
| ©Fangcen Liu. Last update: 2022.01 |
