A new method for retrieving batik shape patterns
针对蜡染中形状图案重复使用且通用检索方法效果不佳的问题，提出一种结合全局和局部特征的新方法，通过加权二分图匹配和度量学习来检索相似形状图案，实验证明优于现有方法。
Batik as a traditional art is well regarded due to its high aesthetic quality and cultural heritage values. It is not uncommon to reuse versatile decorative shape patterns across batiks. General‐purpose image retrieval methods often fail to pay sufficient attention to such a frequent reuse of shape patterns in the graphical compositions of batiks, leading to suboptimal retrieval results, in particular for identifying batiks that use copyrighted shape patterns without proper authorization for law‐enforcement purposes. To address the lack of an optimized image retrieval method suited for batiks, this study proposes a new method for retrieving salient shape patterns in batiks using a rich combination of global and local features. The global features deployed were extracted according to the Zernike moments (ZMs); the local features adopted were extracted through curvelet transformations that characterize shape contours embedded in batiks. The method subsequently incorporated both types of features via matching a weighted bipartite graph to measure the visual similarity between any pair of batik shape patterns through supervised distance metric learning. The derived similarity metric can then be used to detect and retrieve similar shape patterns appearing across batiks, which in turn can be employed as a reliable similarity metric for retrieving batiks. To explore the usefulness of the proposed method, the performance of the new retrieval method is compared against that of three peer methods as well as two variants of the proposed method. The experimental results consistently and convincingly demonstrate that the new method indeed outperforms the state‐of‐the‐art methods in retrieving salient shape patterns in batiks.