%A Ming Chen, Zhifeng Zhang, Tieliang Gao, Li Duan, and Junpeng Zhang %T A Fast and Precise Spatial Verification Strategy for Duplicate Image Retrieval %0 Journal Article %D 2020 %J Int J Performability Eng %R 10.23940/ijpe.20.09.p8.13931403 %P 1393-1403 %V 16 %N 9 %U {https://www.ijpe-online.com/CN/abstract/article_4470.shtml} %8 2020-09-30 %X Spatial verification for duplicate image retrieval is often time-consuming and not sensitive to similar images. To address this problem, we propose a fast and precise spatial verification strategy for duplicate image retrieval. The motivation of this strategy is to use angle and scale information to filter similar images. This is because the matched descriptors of similar images are not transformed according to consistent angles and log-scales. The angle differences and log-scale differences of matched descriptors can be projected to points in two-dimensional space. Intuitively, the two-dimensional point distribution of non-duplicate images is relatively discrete, and the two-dimensional point distribution of duplicate images is relatively concentrated. Therefore, this paper utilizes the inverse cloud algorithm to calculate the discrete degree of the two-dimensional point distribution to exclude the non-duplicate images that have large fluctuation distributions. Then, the new voting algorithm can be used to re-rank the images to improve the retrieval accuracy. The experimental results showed that, compared with traditional algorithms, the new strategy was able to effectively improve retrieval accuracy without adding extra storage overhead and computational overhead.