Content-based image retrieval by segmentation and clustering

Vishal Lonarkar; B. Ashwath Rao

doi:10.1109/ICICI.2017.8365241

Content-based image retrieval by segmentation and clustering

Vishal Lonarkar
, B. Ashwath Rao

School of Computer Engineering

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

5 Citations (Scopus)

Abstract

This paper presents an approach for content-based image retrieval of both texture and non-texture images. With the growth of the number of images in digital format, modern image retrieval systems employ content-based image retrieval. Our system uses automated segmentation technique followed with region based feature extraction. The system employs clustering of images to speed up the retrieval process. The proposed system achieved an accuracy of 97.56%. The result demonstrate that the searching of an image is fast and accurate.

Original language	English
Title of host publication	Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	771-776
Number of pages	6
ISBN (Electronic)	9781538640319
DOIs	https://doi.org/10.1109/ICICI.2017.8365241
Publication status	Published - 24-05-2018
Event	2017 International Conference on Inventive Computing and Informatics, ICICI 2017 - Coimbatore, India Duration: 23-11-2017 → 24-11-2017

Publication series

Name	Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017

Conference

Conference	2017 International Conference on Inventive Computing and Informatics, ICICI 2017
Country/Territory	India
City	Coimbatore
Period	23-11-17 → 24-11-17

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems

Access to Document

10.1109/ICICI.2017.8365241

Cite this

Lonarkar, V., & Rao, B. A. (2018). Content-based image retrieval by segmentation and clustering. In Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017 (pp. 771-776). (Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICICI.2017.8365241

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title = "Content-based image retrieval by segmentation and clustering",

abstract = "This paper presents an approach for content-based image retrieval of both texture and non-texture images. With the growth of the number of images in digital format, modern image retrieval systems employ content-based image retrieval. Our system uses automated segmentation technique followed with region based feature extraction. The system employs clustering of images to speed up the retrieval process. The proposed system achieved an accuracy of 97.56\%. The result demonstrate that the searching of an image is fast and accurate.",

author = "Vishal Lonarkar and Rao, \{B. Ashwath\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Inventive Computing and Informatics, ICICI 2017 ; Conference date: 23-11-2017 Through 24-11-2017",

year = "2018",

month = may,

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doi = "10.1109/ICICI.2017.8365241",

language = "English",

series = "Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017",

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Lonarkar, V & Rao, BA 2018, Content-based image retrieval by segmentation and clustering. in Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017. Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017, Institute of Electrical and Electronics Engineers Inc., pp. 771-776, 2017 International Conference on Inventive Computing and Informatics, ICICI 2017, Coimbatore, India, 23-11-17. https://doi.org/10.1109/ICICI.2017.8365241

Content-based image retrieval by segmentation and clustering. / Lonarkar, Vishal; Rao, B. Ashwath.
Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 771-776 (Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017).

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

TY - GEN

T1 - Content-based image retrieval by segmentation and clustering

AU - Lonarkar, Vishal

AU - Rao, B. Ashwath

PY - 2018/5/24

Y1 - 2018/5/24

N2 - This paper presents an approach for content-based image retrieval of both texture and non-texture images. With the growth of the number of images in digital format, modern image retrieval systems employ content-based image retrieval. Our system uses automated segmentation technique followed with region based feature extraction. The system employs clustering of images to speed up the retrieval process. The proposed system achieved an accuracy of 97.56%. The result demonstrate that the searching of an image is fast and accurate.

AB - This paper presents an approach for content-based image retrieval of both texture and non-texture images. With the growth of the number of images in digital format, modern image retrieval systems employ content-based image retrieval. Our system uses automated segmentation technique followed with region based feature extraction. The system employs clustering of images to speed up the retrieval process. The proposed system achieved an accuracy of 97.56%. The result demonstrate that the searching of an image is fast and accurate.

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BT - Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017

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Lonarkar V, Rao BA. Content-based image retrieval by segmentation and clustering. In Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 771-776. (Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017). doi: 10.1109/ICICI.2017.8365241

Content-based image retrieval by segmentation and clustering

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