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.
Highlights
Dual-ISM, which matches image sequences based on duality, was proposed for efficient similar image searching
Dual-ISM first constructs a feature vector by extracting the feature points from images that are given through the KAZE algorithm
The feature vectors were constructed as data sequences, configured in the form of disjoint windows
Summary
Driven by the rapid growth of image and video data, object recognition, and largescale image searching in real-time videos have received a great deal of attention [1]. These technologies are developing into a variety of applications, such as monitoring and tracking criminal behaviors and continuously tracking specific objects. With the recent development of image-related deep learning technologies, many image and video-related deep learning-based studies are being conducted such as AlexNet [5], RCNN [6], Faster-R-CNN [7], and MATNet [8]. Object features are values that can be used to distinguish one object from another (e.g., color, illuminance, shape, texture, and construction). Features provided in these ways are gradually diversified for accurate object detection, gradually becoming higher-dimensional
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