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학술지 Dual-ISM: Duality-Based Image Sequence Matching for Similar Image Search
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저자
이혜진, 권용진, 임선영
발행일
202202
출처
Applied Sciences, v.12 no.3, pp.1-12
ISSN
2076-3417
출판사
MDPI
DOI
https://dx.doi.org/10.3390/app12031609
협약과제
22HS5100, 장기 시각 메모리 네트워크 기반의 예지형 시각지능 핵심기술 개발, 문진영
초록
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 제안 키워드
Candidate set, Distance calculation, Feature Vector, Image data, Low-dimensional, Subsequence matching, data sequence, extract feature points, image sequence matching, matching method, query image
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