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

Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal

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

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

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10.1109/ICICI.2017.8365241

Cite this

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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}",

year = "2018",

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day = "24",

doi = "10.1109/ICICI.2017.8365241",

language = "English",

pages = "771--776",

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

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2017 International Conference on Inventive Computing and Informatics, ICICI 2017 ; Conference date: 23-11-2017 Through 24-11-2017",

}

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. 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

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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U2 - 10.1109/ICICI.2017.8365241

DO - 10.1109/ICICI.2017.8365241

M3 - Conference contribution

AN - SCOPUS:85048351302

SP - 771

EP - 776

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 International Conference on Inventive Computing and Informatics, ICICI 2017

Y2 - 23 November 2017 through 24 November 2017

ER -

Content-based image retrieval by segmentation and clustering

Abstract

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All Science Journal Classification (ASJC) codes

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