基于多重离散时间量子行走的多类图像重排序算法

Wei-Min shi, Jia-Wei Liang, Xue Zhang, Yihua Zhou
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引用次数: 0

摘要

为了实现多类图像重排序,提出了一种基于多重离散时间量子行走的图像重排序算法。该算法首先构造一个加权无向完全图,图的节点代表图像,这些边的加权值代表图像之间的相似值。其次,利用光谱聚类方法将图像分成k类,并找出每一类的代表图像;第三,利用k个代表图像作为量子系统的初始状态,利用触发器位移算子和加权硬币算子控制加权完全图上的多个离散时间量子行走。最后,将步行者到达图节点的平均概率值作为图像的相关分数,然后根据相关分数对图像进行重新排序。实验结果表明,从视觉评分和相关性评分的比较来看,我们的方案与初始排序算法相比有显著的提升。此外,通过平均精度(AP)和平均平均精度(MAP)来评价算法的有效性,在随机选择的图像组中,对于三种类型的查询图像,我们的算法的AP分别提高了53.21%、31.75%和14.29%,在所有图像组中,我们的算法的MAP比初始排序算法提高了29.57%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-class image reranking algorithm based on multiple discrete-time quantum walk
To achieve multi-class image reranking, a novel image reranking algorithm using multiple discrete-time quantum walk is proposed. In this algorithm, a weighted undirected complete graph is first constructed, in which the nodes for the graph represent the images and the weighted values of these edges are the similarity value between the images. Secondly, it uses the spectral clustering to divide the images into $k$ classes and finds the representative image of each class. Thirdly, it uses the $k$ representative images as the initial state of quantum system, and the flip-flop shift operator and the weighted coin operator are used to control multiple discrete-time quantum walk on the weighted complete graph. Finally, the average probability values of the walker reaching the node of the graph is used as the relevance scores of the image, and then the images are reranked by the relevance scores. the experimental results show that our scheme has a significant enhance compared with the initial ranking algorithm from the comparison of visual and relevance scores. Furthermore, the effectiveness of our algorithm is evaluated by the average precision (AP) and the mean average precision (MAP), where the AP of our algorithm is increased by 53.21%, 31.75% and 14.29% for three types of the query image in randomly selected image group respectively, and the MAP of our algorithm is increased by 29.57% for all image groups compared with the initial ranking algorithm.
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