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