CBIR for an automated solid waste bin level detection system using GLCM

Maher Arebey, M. A. Hannan, R. A. Begum, Hassan Basri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Nowadays, as the amount of waste increases, the need of automated bin collection and level detection becomes more crucial. The paper present an automated bin level detection using gray level co-occurrence matrices (GLCM) based on content-based image retrieval (CBIR). Bhattacharyya and Euclidean distances were used to evaluate CBIR system. The database consisting of different bin images, the database is divided into five classes such as low, medium, full. Flow and overflow. The GLCM features are extracted from both query image and all the images in the database, the output of the query and database images are compared using the similarity distances Bhattacharyya and Euclidean distances. The result shows that Bhattacharyya performs better than Euclidean in retrieving the top 20 images that are close to the query image. The performance of the automated bin level detection system using GLCM and CBIR system reached 0.716. The combination between the two techniques proved to be efficient and robust. © 2011 Springer-Verlag.
Original languageEnglish
Title of host publicationCBIR for an automated solid waste bin level detection system using GLCM
Pages280-288
Number of pages251
ISBN (Electronic)9783642251900
DOIs
Publication statusPublished - 21 Nov 2011
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 11 Nov 2013 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7066 LNCS
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period11/11/13 → …

Fingerprint

Bins
Image retrieval
Solid wastes

Cite this

Arebey, M., Hannan, M. A., Begum, R. A., & Basri, H. (2011). CBIR for an automated solid waste bin level detection system using GLCM. In CBIR for an automated solid waste bin level detection system using GLCM (pp. 280-288). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7066 LNCS). https://doi.org/10.1007/978-3-642-25191-7_27
Arebey, Maher ; Hannan, M. A. ; Begum, R. A. ; Basri, Hassan. / CBIR for an automated solid waste bin level detection system using GLCM. CBIR for an automated solid waste bin level detection system using GLCM. 2011. pp. 280-288 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{b11c0dbab7de4082a4d3ea57cff545d8,
title = "CBIR for an automated solid waste bin level detection system using GLCM",
abstract = "Nowadays, as the amount of waste increases, the need of automated bin collection and level detection becomes more crucial. The paper present an automated bin level detection using gray level co-occurrence matrices (GLCM) based on content-based image retrieval (CBIR). Bhattacharyya and Euclidean distances were used to evaluate CBIR system. The database consisting of different bin images, the database is divided into five classes such as low, medium, full. Flow and overflow. The GLCM features are extracted from both query image and all the images in the database, the output of the query and database images are compared using the similarity distances Bhattacharyya and Euclidean distances. The result shows that Bhattacharyya performs better than Euclidean in retrieving the top 20 images that are close to the query image. The performance of the automated bin level detection system using GLCM and CBIR system reached 0.716. The combination between the two techniques proved to be efficient and robust. {\circledC} 2011 Springer-Verlag.",
author = "Maher Arebey and Hannan, {M. A.} and Begum, {R. A.} and Hassan Basri",
year = "2011",
month = "11",
day = "21",
doi = "10.1007/978-3-642-25191-7_27",
language = "English",
isbn = "9783642251900",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "280--288",
booktitle = "CBIR for an automated solid waste bin level detection system using GLCM",

}

Arebey, M, Hannan, MA, Begum, RA & Basri, H 2011, CBIR for an automated solid waste bin level detection system using GLCM. in CBIR for an automated solid waste bin level detection system using GLCM. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7066 LNCS, pp. 280-288, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11/11/13. https://doi.org/10.1007/978-3-642-25191-7_27

CBIR for an automated solid waste bin level detection system using GLCM. / Arebey, Maher; Hannan, M. A.; Begum, R. A.; Basri, Hassan.

CBIR for an automated solid waste bin level detection system using GLCM. 2011. p. 280-288 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7066 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - CBIR for an automated solid waste bin level detection system using GLCM

AU - Arebey, Maher

AU - Hannan, M. A.

AU - Begum, R. A.

AU - Basri, Hassan

PY - 2011/11/21

Y1 - 2011/11/21

N2 - Nowadays, as the amount of waste increases, the need of automated bin collection and level detection becomes more crucial. The paper present an automated bin level detection using gray level co-occurrence matrices (GLCM) based on content-based image retrieval (CBIR). Bhattacharyya and Euclidean distances were used to evaluate CBIR system. The database consisting of different bin images, the database is divided into five classes such as low, medium, full. Flow and overflow. The GLCM features are extracted from both query image and all the images in the database, the output of the query and database images are compared using the similarity distances Bhattacharyya and Euclidean distances. The result shows that Bhattacharyya performs better than Euclidean in retrieving the top 20 images that are close to the query image. The performance of the automated bin level detection system using GLCM and CBIR system reached 0.716. The combination between the two techniques proved to be efficient and robust. © 2011 Springer-Verlag.

AB - Nowadays, as the amount of waste increases, the need of automated bin collection and level detection becomes more crucial. The paper present an automated bin level detection using gray level co-occurrence matrices (GLCM) based on content-based image retrieval (CBIR). Bhattacharyya and Euclidean distances were used to evaluate CBIR system. The database consisting of different bin images, the database is divided into five classes such as low, medium, full. Flow and overflow. The GLCM features are extracted from both query image and all the images in the database, the output of the query and database images are compared using the similarity distances Bhattacharyya and Euclidean distances. The result shows that Bhattacharyya performs better than Euclidean in retrieving the top 20 images that are close to the query image. The performance of the automated bin level detection system using GLCM and CBIR system reached 0.716. The combination between the two techniques proved to be efficient and robust. © 2011 Springer-Verlag.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=81255205052&origin=inward

UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=81255205052&origin=inward

U2 - 10.1007/978-3-642-25191-7_27

DO - 10.1007/978-3-642-25191-7_27

M3 - Conference contribution

SN - 9783642251900

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 280

EP - 288

BT - CBIR for an automated solid waste bin level detection system using GLCM

ER -

Arebey M, Hannan MA, Begum RA, Basri H. CBIR for an automated solid waste bin level detection system using GLCM. In CBIR for an automated solid waste bin level detection system using GLCM. 2011. p. 280-288. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-25191-7_27