Adaptive region matching for region‐based image retrieval by constructing region importance index. Issue 2 (1st April 2014)

• Record Type: Journal Article
• Authors: Yang, Xiaohui; Cai, Lijun
• Is Part Of: IET computer vision. Volume 8:Issue 2(2014)
• Page Start: 141
• Page End: 151
• Publication Date: 2014-04-01
• Subjects: image matching -- image retrieval -- interference -- visual databases; Corel-1000 database -- UW database -- Caltech-256 database -- image-to-image matching -- SMR-to-image matching -- semantic meaningful region -- interference regions -- ARM -- visual similarity measurement -- RBIR -- similarity matching problem -- region importance index -- region-based image retrieval -- adaptive region matching; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2012.0157
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : This study deals with the problem of similarity matching in region‐based image retrieval (RBIR). A novel visual similarity measurement called adaptive region matching (ARM) has been developed. For decreasing negative influence of interference regions and important information loss simultaneously, a region importance index is constructed and semantic meaningful region (SMR) is introduced. Moreover, ARM automatically performs SMR‐to‐image matching or image‐to‐image matching. Extensive experiments on Corel‐1000, Caltech‐256 and University of Washington (UW) databases demonstrate the authors proposed ARM is more flexible and more efficient than the existing visual similarity measurements that were originally developed for RBIR.