用分形三角搜索法加速图像内容查找，效率显著优于现有方法。
Fractal triangular search: a metaheuristic for image content search
- 基于分形三角链的局部搜索策略，动态调整搜索方向与范围。
- 在九组实验中平均提速超8%，七组中平均提速超22%。
- 适合大规模图像内容检索，尤其在图像尺寸增大时优势更明显。
本文提出一种基于分形的变邻域搜索算法（FTS），专为图像内容搜索设计。将特定内容搜索建模为优化问题，其中证据元素应存在且与目标空间位置紧密相关。所提算法采用不断嵌套并无限增长的三角链结构，每轮迭代中方向动态变化，形成分形式搜索路径。作者进行了大量实验，结果表明FTS显著优于当前最优元启发式方法。第一组实验中，9个案例中有7个更快，平均提速>8%；第二组实验中，7个案例中有6个更快，平均提速>22%。随着图像尺寸增大，FTS相对于其他元启发式方法的性能优势愈发明显。
原文摘要 · Abstract (English)
This work proposes a variable neighbourhood search (FTS) that uses a fractal-based local search primarily designed for images. Searching for specific content in images is posed as an optimisation problem, where evidence elements are expected to be present. Evidence elements improve the odds of finding the desired content and are closely associated to it in terms of spatial location. The proposed local search algorithm follows the fashion of a chain of triangles that engulf each other and grow indefinitely in a fractal fashion, while their orientation varies in each iteration. The authors carried out an extensive set of experiments, which confirmed that FTS outperforms state-of-the-art metaheuristics. On average, FTS was able to locate content faster, visiting less incorrect image locations. In the first group of experiments, FTS was faster in seven out of nine cases, being >8% faster on average, when compared to the second best search method. In the second group, FTS was faster in six out of seven cases, and it was >22% faster on average when compared to the approach ranked second best. FTS tends to outperform other metaheuristics substantially as the size of the image increases.
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