Content Based Image Retrieval Using HDMR Constant Term Based Clustering

Ayşegül Karcılı; Burcu Tunga

doi:10.1007/978-3-030-96401-6_3

Content Based Image Retrieval Using HDMR Constant Term Based Clustering

Ayşegül Karcılı, Burcu Tunga^*

^*Corresponding author for this work

Department of Mathematics Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The studies related with the content-based image retrieval (CBIR) has increased because of both necessity for efficient image retrieval and the limitations in large-scale systems. Efficient image retrieval refers to finding accurate image from the database with high speed. This paper presents a new efficient image retrieval method using High Dimensional Model Representation (HDMR). The method has two main steps, clustering and retrieval. In clustering part, we use k-means method on HDMR constant term while in the subsequent part, we retrieve the most similar images to a given query image from a relevant cluster. We experiment the efficiency and effectiveness of the new algorithm on Columbia Object Image Library (COIL-100) and get conspicuous results. These results are tabulated in the paper.

Original language	English
Title of host publication	Mathematical Methods for Engineering Applications - ICMASE 2021
Editors	Fatih Yilmaz, Araceli Queiruga-Dios, María Jesús Santos Sánchez, Deolinda Rasteiro, Víctor Gayoso Martínez, Jesús Martín Vaquero
Publisher	Springer
Pages	35-45
Number of pages	11
ISBN (Print)	9783030964009
DOIs	https://doi.org/10.1007/978-3-030-96401-6_3
Publication status	Published - 2022
Event	2nd International Conference on Mathematics and its Applications in Science and Engineering, ICMASE 2021 - Salamanca, Spain Duration: 1 Jul 2021 → 2 Jul 2021

Publication series

Name	Springer Proceedings in Mathematics and Statistics
Volume	384
ISSN (Print)	2194-1009
ISSN (Electronic)	2194-1017

Conference

Conference	2nd International Conference on Mathematics and its Applications in Science and Engineering, ICMASE 2021
Country/Territory	Spain
City	Salamanca
Period	1/07/21 → 2/07/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Funding

Acknowledgements This work was supported by Research Fund of the Istanbul Technical University with project number 41411.

Funders	Funder number
Istanbul Teknik Üniversitesi	41411

Keywords

Clustering
HDMR
Image decomposition
Image retrieval
k-means

Access to Document

10.1007/978-3-030-96401-6_3

Cite this

Karcılı, A., & Tunga, B. (2022). Content Based Image Retrieval Using HDMR Constant Term Based Clustering. In F. Yilmaz, A. Queiruga-Dios, M. J. Santos Sánchez, D. Rasteiro, V. Gayoso Martínez, & J. Martín Vaquero (Eds.), Mathematical Methods for Engineering Applications - ICMASE 2021 (pp. 35-45). (Springer Proceedings in Mathematics and Statistics; Vol. 384). Springer. https://doi.org/10.1007/978-3-030-96401-6_3

Karcılı, Ayşegül ; Tunga, Burcu. / Content Based Image Retrieval Using HDMR Constant Term Based Clustering. Mathematical Methods for Engineering Applications - ICMASE 2021. editor / Fatih Yilmaz ; Araceli Queiruga-Dios ; María Jesús Santos Sánchez ; Deolinda Rasteiro ; Víctor Gayoso Martínez ; Jesús Martín Vaquero. Springer, 2022. pp. 35-45 (Springer Proceedings in Mathematics and Statistics).

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abstract = "The studies related with the content-based image retrieval (CBIR) has increased because of both necessity for efficient image retrieval and the limitations in large-scale systems. Efficient image retrieval refers to finding accurate image from the database with high speed. This paper presents a new efficient image retrieval method using High Dimensional Model Representation (HDMR). The method has two main steps, clustering and retrieval. In clustering part, we use k-means method on HDMR constant term while in the subsequent part, we retrieve the most similar images to a given query image from a relevant cluster. We experiment the efficiency and effectiveness of the new algorithm on Columbia Object Image Library (COIL-100) and get conspicuous results. These results are tabulated in the paper.",

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Karcılı, A & Tunga, B 2022, Content Based Image Retrieval Using HDMR Constant Term Based Clustering. in F Yilmaz, A Queiruga-Dios, MJ Santos Sánchez, D Rasteiro, V Gayoso Martínez & J Martín Vaquero (eds), Mathematical Methods for Engineering Applications - ICMASE 2021. Springer Proceedings in Mathematics and Statistics, vol. 384, Springer, pp. 35-45, 2nd International Conference on Mathematics and its Applications in Science and Engineering, ICMASE 2021, Salamanca, Spain, 1/07/21. https://doi.org/10.1007/978-3-030-96401-6_3

Content Based Image Retrieval Using HDMR Constant Term Based Clustering. / Karcılı, Ayşegül; Tunga, Burcu.
Mathematical Methods for Engineering Applications - ICMASE 2021. ed. / Fatih Yilmaz; Araceli Queiruga-Dios; María Jesús Santos Sánchez; Deolinda Rasteiro; Víctor Gayoso Martínez; Jesús Martín Vaquero. Springer, 2022. p. 35-45 (Springer Proceedings in Mathematics and Statistics; Vol. 384).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Content Based Image Retrieval Using HDMR Constant Term Based Clustering

AU - Karcılı, Ayşegül

AU - Tunga, Burcu

PY - 2022

Y1 - 2022

N2 - The studies related with the content-based image retrieval (CBIR) has increased because of both necessity for efficient image retrieval and the limitations in large-scale systems. Efficient image retrieval refers to finding accurate image from the database with high speed. This paper presents a new efficient image retrieval method using High Dimensional Model Representation (HDMR). The method has two main steps, clustering and retrieval. In clustering part, we use k-means method on HDMR constant term while in the subsequent part, we retrieve the most similar images to a given query image from a relevant cluster. We experiment the efficiency and effectiveness of the new algorithm on Columbia Object Image Library (COIL-100) and get conspicuous results. These results are tabulated in the paper.

AB - The studies related with the content-based image retrieval (CBIR) has increased because of both necessity for efficient image retrieval and the limitations in large-scale systems. Efficient image retrieval refers to finding accurate image from the database with high speed. This paper presents a new efficient image retrieval method using High Dimensional Model Representation (HDMR). The method has two main steps, clustering and retrieval. In clustering part, we use k-means method on HDMR constant term while in the subsequent part, we retrieve the most similar images to a given query image from a relevant cluster. We experiment the efficiency and effectiveness of the new algorithm on Columbia Object Image Library (COIL-100) and get conspicuous results. These results are tabulated in the paper.

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KW - Image decomposition

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BT - Mathematical Methods for Engineering Applications - ICMASE 2021

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A2 - Rasteiro, Deolinda

A2 - Gayoso Martínez, Víctor

A2 - Martín Vaquero, Jesús

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Karcılı A, Tunga B. Content Based Image Retrieval Using HDMR Constant Term Based Clustering. In Yilmaz F, Queiruga-Dios A, Santos Sánchez MJ, Rasteiro D, Gayoso Martínez V, Martín Vaquero J, editors, Mathematical Methods for Engineering Applications - ICMASE 2021. Springer. 2022. p. 35-45. (Springer Proceedings in Mathematics and Statistics). doi: 10.1007/978-3-030-96401-6_3

Content Based Image Retrieval Using HDMR Constant Term Based Clustering

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

Access to Document

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