Affiliations: [a] School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| [b] School of Computer Science, Hubei University of Technology, Wuhan, China
| [c] Department of Economics, Ohio State University, Columbus, OH, USA
Correspondence:
[*]
Corresponding author. Xianqiao Chen, School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430062, China. E-mail: [email protected].
Abstract: The traditional re-recognition algorithm needs to find or design the characteristics with better robustness to light, scale, and deformation. The quality of the feature directly affects the recognition performance and the uncertainty is high. In addition, it needs supervision and training, and has the higher training time and space complexity. To address this problem, a new intelligent re-recognition algorithm for specific ship target in busy waters under the actual scene is proposed in this paper. Combining the existing feature extraction model and graph model, the graph structure is used to describe the identity relationship between the samples. Two points with side connections have the same identity label. Then the multi-layer graph structure is built. After obtaining the block of the divided area, the similarity between the two samples of the link is calculated and the weight of the edge is obtained. Labeled samples are built according to the selected initial area. The energy loss of the graph model is obtained by estimating the pixel likelihood energy function with different labels of pixels and areas. A graph structure is obtained by minimizing the energy loss, which is the intelligent recognition result of specific ship target. For the large-scale data, the problem of incremental processing is solved by incremental maintenance. Experimental results show that the proposed algorithm has high recognition precision.
Keywords: Actual scene, busy waters, specific ship target, intelligence, re-recognition
