PixSearcher: Searching similar images in large image collections through pixel descriptors

Tuan Nhon Dang; Leland Wilkinson

doi:10.1007/978-3-319-14364-4_70

PixSearcher: Searching similar images in large image collections through pixel descriptors

Tuan Nhon Dang, Leland Wilkinson

Computer Science

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

2 Scopus citations

Abstract

Searching and mining huge image databases has become a daunting task for many application areas such as astronomy, medicine, geology, oceanography, and crime prevention. We introduce a new image indexing and retrieval system, PixSearcher, that computes image descriptors from pixels obtained by thresholding images at different levels. In contrast to raster techniques (which use the entire pixel raster for distance computations), PixSearcher uses a small set of descriptors and consequently can handle large image collections within reasonable time. We use benchmark image databases from different domains to evaluate PixSearcher’s performance versus well-known image retrieval techniques.

Original language	English
Title of host publication	Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings
Editors	George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Ryan McMahan, Jason Jerald, Hui Zhang, Steven M. Drucker, Kambhamettu Chandra, El Choubassi Maha, Zhigang Deng, Mark Carlson
Publisher	Springer-Verlag
Pages	726-735
Number of pages	10
ISBN (Electronic)	9783319143637
DOIs	https://doi.org/10.1007/978-3-319-14364-4_70
State	Published - 2014
Event	10th International Symposium on Visual Computing, ISVC 2014 - Las Vegas, United States Duration: Dec 8 2014 → Dec 10 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8888
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Symposium on Visual Computing, ISVC 2014
Country	United States
City	Las Vegas
Period	12/8/14 → 12/10/14

Access to Document

10.1007/978-3-319-14364-4_70

Link to publication in Scopus

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Dang, T. N., & Wilkinson, L. (2014). PixSearcher: Searching similar images in large image collections through pixel descriptors. In G. Bebis, R. Boyle, B. Parvin, D. Koracin, R. McMahan, J. Jerald, H. Zhang, S. M. Drucker, K. Chandra, E. C. Maha, Z. Deng, & M. Carlson (Eds.), Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings (pp. 726-735). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8888). Springer-Verlag. https://doi.org/10.1007/978-3-319-14364-4_70

Dang, Tuan Nhon ; Wilkinson, Leland. / PixSearcher : Searching similar images in large image collections through pixel descriptors. Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings. editor / George Bebis ; Richard Boyle ; Bahram Parvin ; Darko Koracin ; Ryan McMahan ; Jason Jerald ; Hui Zhang ; Steven M. Drucker ; Kambhamettu Chandra ; El Choubassi Maha ; Zhigang Deng ; Mark Carlson. Springer-Verlag, 2014. pp. 726-735 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Searching and mining huge image databases has become a daunting task for many application areas such as astronomy, medicine, geology, oceanography, and crime prevention. We introduce a new image indexing and retrieval system, PixSearcher, that computes image descriptors from pixels obtained by thresholding images at different levels. In contrast to raster techniques (which use the entire pixel raster for distance computations), PixSearcher uses a small set of descriptors and consequently can handle large image collections within reasonable time. We use benchmark image databases from different domains to evaluate PixSearcher{\textquoteright}s performance versus well-known image retrieval techniques.",

author = "Dang, {Tuan Nhon} and Leland Wilkinson",

year = "2014",

doi = "10.1007/978-3-319-14364-4_70",

language = "English",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "726--735",

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booktitle = "Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings",

note = "null ; Conference date: 08-12-2014 Through 10-12-2014",

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Dang, TN & Wilkinson, L 2014, PixSearcher: Searching similar images in large image collections through pixel descriptors. in G Bebis, R Boyle, B Parvin, D Koracin, R McMahan, J Jerald, H Zhang, SM Drucker, K Chandra, EC Maha, Z Deng & M Carlson (eds), Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8888, Springer-Verlag, pp. 726-735, 10th International Symposium on Visual Computing, ISVC 2014, Las Vegas, United States, 12/8/14. https://doi.org/10.1007/978-3-319-14364-4_70

PixSearcher : Searching similar images in large image collections through pixel descriptors. / Dang, Tuan Nhon; Wilkinson, Leland.

Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings. ed. / George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Ryan McMahan; Jason Jerald; Hui Zhang; Steven M. Drucker; Kambhamettu Chandra; El Choubassi Maha; Zhigang Deng; Mark Carlson. Springer-Verlag, 2014. p. 726-735 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8888).

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

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AB - Searching and mining huge image databases has become a daunting task for many application areas such as astronomy, medicine, geology, oceanography, and crime prevention. We introduce a new image indexing and retrieval system, PixSearcher, that computes image descriptors from pixels obtained by thresholding images at different levels. In contrast to raster techniques (which use the entire pixel raster for distance computations), PixSearcher uses a small set of descriptors and consequently can handle large image collections within reasonable time. We use benchmark image databases from different domains to evaluate PixSearcher’s performance versus well-known image retrieval techniques.

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Dang TN, Wilkinson L. PixSearcher: Searching similar images in large image collections through pixel descriptors. In Bebis G, Boyle R, Parvin B, Koracin D, McMahan R, Jerald J, Zhang H, Drucker SM, Chandra K, Maha EC, Deng Z, Carlson M, editors, Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings. Springer-Verlag. 2014. p. 726-735. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-14364-4_70