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Journal Article Dual-ISM: Duality-Based Image Sequence Matching for Similar Image Search
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Authors
Hye-Jin Lee, Yongjin Kwon, Sun-Young Ihm
Issue Date
2022-02
Citation
Applied Sciences, v.12, no.3, pp.1-12
ISSN
2076-3417
Publisher
MDPI
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.3390/app12031609
Project Code
22HS5100, Development of Previsional Intelligence based on Long-term Visual Memory Network, Moon Jin Young
Abstract
In this paper, we propose the duality-based image sequence matching method, which is called Dual-ISM, a subsequence matching method for searching for similar images. We first extract feature points from the given image data and configure the feature vectors as one data sequence. Next, the feature vectors are configured in the form of a disjoint window, and a low-dimensional transformation is carried out. Subsequently, the query image that is entered to construct the candidate set is similarly subjected to a low-dimensional transformation, and the low-dimensional transformed window of the data sequence and window that are less than the allowable value, 琯, is regarded as the candidate set using a distance calculation. Finally, similar images are searched in the candidate set using the distance calculation that are based on the original feature vector.
KSP Keywords
Candidate set, Distance calculation, Feature Vector, Image data, Low-dimensional, Subsequence matching, data sequence, extract feature points, image sequence matching, matching method, query image
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
CC BY