A time-series matching approach for symmetric-invariant boundary image matching

• Published in: Multimedia tools and applications Vol. 77; no. 16; pp. 20979 - 21001
• Main Authors: Lee, Sanghun, Kim, Hajin, Choi, Mi-Jung, Moon, Yang-Sae
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2018; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Data Structures and Information Theory; Invariance; Invariants; Matching; Methods; Multimedia Information Systems; Production methods; Special Purpose and Application-Based Systems; Time-series matching; Image time-series; Rotation invariance; Symmetric invariance; Boundary image matching
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-017-5323-4

In this paper, we address the problem of boundary image matching that supports symmetric invariance. Supporting the symmetric invariance is an important factor to provide more intuitive and more correct results in boundary image matching. Previous boundary image matching methods, however, deal with mainly image rotations without consideration of symmetric transformations. In this paper, we propose a time-series-based boundary image matching that supports the symmetric invariance as well as the previous rotation invariance. For this, we first formally define the concept of a boundary time-series and its symmetric time-series. We then present a novel notion of symmetric-rotation property that the rotation-invariant matching result is always the same for all possible symmetric angles. We next discuss how to efficiently extract a symmetric time-series from an image boundary by presenting the domain independent property that both time-series domain and image domain methods produce the same symmetric time-series. Experimental results show that the proposed symmetric-invariant matching provides the more intuitive result compared with the previous rotation-invariant matching. To our best knowledge, this is the first attempt that solves the symmetric-invariant boundary matching problem in the simple time-series domain rather than in the complex image domain.