Dual-ISM: Duality-Based Image Sequence Matching for Similar Image Search
Source abstract
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, 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Medical Image Segmentation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Engineering Research Center, Pai Chai University, 155-40 Baejae-ro, Seo-gu, Daejeon 35345, Korea
Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
Department of Computer Engineering, Pai Chai University, 155-40 Baejae-ro, Seo-gu, Daejeon 35345, Korea
Funding
Institute for Information and Communications Technology Promotion
References (28)
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