Dual-ISM: Duality-Based Image Sequence Matching for Similar Image Search

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 ve...

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Bibliographic Details
Published inApplied sciences Vol. 12; no. 3; p. 1609
Main Authors Lee, Hye-Jin, Kwon, Yongjin, Ihm, Sun-Young
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2022
Subjects
Deep learning
Discriminant analysis
image sequence matching
Queries
similar image search
subsequence matching
Time series
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Summary: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, ε, 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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12031609