Abstract
This paper proposes a re-ranking method for vehicle re-identification based on k-reciprocal Encoding. In recent years, with the development and popularization of deep learning, vehicle re-identification has made great progress and excellent performance. The re-ranking me-thod applied to re-identification has been widely adopted and recognized. However, the existing re-ranking methods still have room to be optimized. The existing k-reciprocal encoding based re-ranking method considers that if the k-nearest neighbor (k-nn) of a gallery image includes the probe image, it is more likely to be a positive matching result, and we called these gallery images are the k-reciprocal nearest neighbors (k-rnn) of the probe image. By encoding its k-rnn into a single vector to calculate the k-reciprocal features, and use this vector to re-ranking by the Jaccard distance. The final distance is calculated as a combination of the original distance and the Jaccard distance. Our idea is that when k-rnn are encoded as a single vector, the k-rnn with a higher-ranking should be given a higher weight, and the k-rnn with a lower-ranking should be given a lower weight. This method will make matching results that are more likely to be a positive result get a higher ranking. In this paper, we use the above idea to assign weights to k-rnn to re-ranking the results of re-ID.
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Liu, S., Konishi, Y., Miyao, J., Kurita, T. (2022). Optimization of Re-ranking Based on k-Reciprocal for Vehicle Re-identification. In: Sumi, K., Na, I.S., Kaneko, N. (eds) Frontiers of Computer Vision. IW-FCV 2022. Communications in Computer and Information Science, vol 1578. Springer, Cham. https://doi.org/10.1007/978-3-031-06381-7_21
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