Low-level feature image retrieval using representative images from minimum spanning tree clustering

Typical content-based image retrieval systems retrieve images based on comparison of low-level features such as images color, texture, and shapes of objects in the images. Further, the image covariance descriptor (CD) and the image Patch Relational Covariance Descriptor (PRCD) can be used to summari...

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Vydáno v:Multimedia tools and applications Ročník 83; číslo 2; s. 3335 - 3356
Hlavní autoři: Khunsongkiet, Piyavach, Bootkrajang, Jakramate, Techawut, Churee
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.01.2024
Springer Nature B.V
Témata:
Algorithms
Clustering
Computer Communication Networks
Computer Science
Covariance
Data Structures and Information Theory
Graph theory
Image retrieval
Multimedia
Multimedia Information Systems
Retrieval
Special Purpose and Application-Based Systems
Patch Relational Covariance Descriptor
Image retrieval
Representative image retrieval
Image clustering and merging
Low-level visual feature
Patch arrangement vision
Region covariance matrix
ISSN:1380-7501, 1573-7721
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Shrnutí:Typical content-based image retrieval systems retrieve images based on comparison of low-level features such as images color, texture, and shapes of objects in the images. Further, the image covariance descriptor (CD) and the image Patch Relational Covariance Descriptor (PRCD) can be used to summarize low–level features and the visual arrangement to improve the precision of the retrieval. Nonetheless, comparing images based on those two descriptors is computationally expensive. Therefore, this research proposes a clustering method that dynamically groups database images using the Minimum Spanning Tree Clustering algorithm (MSTC). The technique is named Representative Images from Minimum Spanning Tree Clustering (RIMSTC). In the proposed technique, only the representative images selected from each cluster are compared with the input image . Experimental results demonstrated that the proposed representative images by COV and PRCD combined with RIMSTC helps to improve the retrieval time while maintaining comparable retrieval performance to existing methods.
Bibliografie:ObjectType-Article-1
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-023-15605-5