A new method for retrieving batik shape patterns

Qingni Yuan, Songhua Xu, Lv Jian

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)578-599
Number of pages22
JournalJournal of the Association for Information Science and Technology
Volume69
Issue number4
DOIs
StatePublished - Apr 2018

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences

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