Efficient descriptors selection in automatic image retrieval using DENOL

A well-structured and indexed database alleviates the computing burden on large data. This paper describes groundwork for presenting the data in a compact, distinctive form, improving the procedures of applying keypoint detection algorithms to preprocess and reduce the relevant features of the image...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2022-01, Vol.43 (2), p.1739-1749
Main Authors: Ignat, Anca, Luca, Mihaela, Păvăloi, Ioan, Lazăr, Camelia
Format: Article
Language:English
Subjects:
Algorithms
Computational efficiency
Computing time
Hessian matrices
Image management
Retrieval
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Summary:A well-structured and indexed database alleviates the computing burden on large data. This paper describes groundwork for presenting the data in a compact, distinctive form, improving the procedures of applying keypoint detection algorithms to preprocess and reduce the relevant features of the images. Our method computes for an image a number of SURF keypoints in a given interval, by adapting the threshold related to the Hessian matrix blob detector. This type of approach allows selecting the level of detail to use in image description and gives us control over the computing time. We named this method DENOL (Descriptor Number On Limits) and tested it on images from two datasets, UCID and an original image database which we propose, IIT_DB. Very good retrieval results and a significantly reduced computing time are achieved.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-219275