Pill-ID: Matching and retrieval of drug pill images

Young Beom Lee; Unsang Park; Anil K. Jain; Seong Whan Lee

doi:10.1016/j.patrec.2011.08.022

Pill-ID: Matching and retrieval of drug pill images

Young Beom Lee, Unsang Park, Anil K. Jain, Seong Whan Lee

Department of Artificial Intelligence

Research output: Contribution to journal › Article › peer-review

38 Citations (Scopus)

Abstract

Worldwide, law enforcement agencies are encountering a substantial increase in the number of illicit drug pills being circulated in our society. Identifying the source and manufacturer of these illicit drugs will help deter drug-related crimes. We have developed an automatic system, called Pill-ID to match drug pill images based on several features (i.e.; imprint, color, and shape) of the tablet. The color and shape information is encoded as a three-dimensional histogram and invariant moments, respectively. The imprint on the pill is encoded as feature vectors derived from SIFT and MLBP descriptors. Experimental results using a database of drug pill images (1029 illicit drug pill images and 14,002 legal drug pill images) show 73.04% (84.47%) rank-1 (rank-20) retrieval accuracy.

Original language	English
Pages (from-to)	904-910
Number of pages	7
Journal	Pattern Recognition Letters
Volume	33
Issue number	7
DOIs	https://doi.org/10.1016/j.patrec.2011.08.022
Publication status	Published - 2012 May 1

Keywords

Color histogram
Illicit drugs
Image retrieval
Imprints
Moment invariants
Pill images

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.patrec.2011.08.022

Cite this

@article{4038c432a8b64998ba783eb6205160ff,

title = "Pill-ID: Matching and retrieval of drug pill images",

abstract = "Worldwide, law enforcement agencies are encountering a substantial increase in the number of illicit drug pills being circulated in our society. Identifying the source and manufacturer of these illicit drugs will help deter drug-related crimes. We have developed an automatic system, called Pill-ID to match drug pill images based on several features (i.e.; imprint, color, and shape) of the tablet. The color and shape information is encoded as a three-dimensional histogram and invariant moments, respectively. The imprint on the pill is encoded as feature vectors derived from SIFT and MLBP descriptors. Experimental results using a database of drug pill images (1029 illicit drug pill images and 14,002 legal drug pill images) show 73.04% (84.47%) rank-1 (rank-20) retrieval accuracy.",

keywords = "Color histogram, Illicit drugs, Image retrieval, Imprints, Moment invariants, Pill images",

author = "Lee, {Young Beom} and Unsang Park and Jain, {Anil K.} and Lee, {Seong Whan}",

note = "Funding Information: The authors thank Dr. Mark Tahtouh of the Australian Federal Police for providing drug pill images. Anil K. Jain{\textquoteright}s research was partially supported by WCU (World Class University) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology ( R31-10008 ) to Korea university.",

year = "2012",

month = may,

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doi = "10.1016/j.patrec.2011.08.022",

language = "English",

volume = "33",

pages = "904--910",

journal = "Pattern Recognition Letters",

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T2 - Matching and retrieval of drug pill images

AU - Lee, Young Beom

AU - Park, Unsang

AU - Jain, Anil K.

AU - Lee, Seong Whan

N1 - Funding Information: The authors thank Dr. Mark Tahtouh of the Australian Federal Police for providing drug pill images. Anil K. Jain’s research was partially supported by WCU (World Class University) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology ( R31-10008 ) to Korea university.

PY - 2012/5/1

Y1 - 2012/5/1

N2 - Worldwide, law enforcement agencies are encountering a substantial increase in the number of illicit drug pills being circulated in our society. Identifying the source and manufacturer of these illicit drugs will help deter drug-related crimes. We have developed an automatic system, called Pill-ID to match drug pill images based on several features (i.e.; imprint, color, and shape) of the tablet. The color and shape information is encoded as a three-dimensional histogram and invariant moments, respectively. The imprint on the pill is encoded as feature vectors derived from SIFT and MLBP descriptors. Experimental results using a database of drug pill images (1029 illicit drug pill images and 14,002 legal drug pill images) show 73.04% (84.47%) rank-1 (rank-20) retrieval accuracy.

AB - Worldwide, law enforcement agencies are encountering a substantial increase in the number of illicit drug pills being circulated in our society. Identifying the source and manufacturer of these illicit drugs will help deter drug-related crimes. We have developed an automatic system, called Pill-ID to match drug pill images based on several features (i.e.; imprint, color, and shape) of the tablet. The color and shape information is encoded as a three-dimensional histogram and invariant moments, respectively. The imprint on the pill is encoded as feature vectors derived from SIFT and MLBP descriptors. Experimental results using a database of drug pill images (1029 illicit drug pill images and 14,002 legal drug pill images) show 73.04% (84.47%) rank-1 (rank-20) retrieval accuracy.

KW - Color histogram

KW - Illicit drugs

KW - Image retrieval

KW - Imprints

KW - Moment invariants

KW - Pill images

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DO - 10.1016/j.patrec.2011.08.022

M3 - Article

AN - SCOPUS:84858437554

SN - 0167-8655

VL - 33

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EP - 910

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

IS - 7

ER -

Pill-ID: Matching and retrieval of drug pill images

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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