A Novel Approach for Real-Time Vehicle Re-identification Using Content-Based Image Retrieval with Relevance Feedback
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-15175-0_16
Reference15 articles.
1. He K, Zhang X, Ren S, Sun J: Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. 770–778).
2. Padmakumar, V, et al. “A robust approach to open vocabulary image retrieval with deep convolutional neural networks and transfer learning.” 2018 Pacific Neighborhood Consortium Annual Conf. and Joint Meetings (PNC). IEEE, 2018.
3. Karthik, K., and S. Sowmya Kamath. “A deep neural network model for content-based medical image retrieval with multi-view classification.” The Visual Computer 37, no. 7 (2021): 1837–1850.
4. Kumar, Niteesh, et al. “Sketch-Based Image Retrieval Using Convolutional Neural Networks Based on Feature Adaptation and Relevance Feedback.” Intl. Conf. on Emerging Applications of Information Technology. Springer, Singapore, 2021.
5. Xinchen Liu, Wu Liu, Tao Mei, Huadong Ma: PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance. IEEE Trans. Multimedia 20(3): 645–658 (2018)
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