A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval

Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learni...

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Main Authors: LIU, Yang, JIN, Rong, Mummert, Lily, Sukthankar, Rahul, Goode, Adam, ZHENG, Bin, HOI, Steven C. H., Satyanarayanan, Mahadev
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Language:English
Published: Institutional Knowledge at Singapore Management University 2010
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Online Access:https://ink.library.smu.edu.sg/sis_research/2315
https://ink.library.smu.edu.sg/context/sis_research/article/3315/viewcontent/A_Boosting_Framework_for_Visuality_Preserving_Dist.pdf
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spelling sg-smu-ink.sis_research-33152018-12-05T09:34:15Z A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval LIU, Yang JIN, Rong Mummert, Lily Sukthankar, Rahul Goode, Adam ZHENG, Bin HOI, Steven C. H. Satyanarayanan, Mahadev Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming distance using the learned binary representation. A boosting algorithm is presented to efficiently learn the distance function. We evaluate the proposed algorithm on a mammographic image reference library with an Interactive Search-Assisted Decision Support (ISADS) system and on the medical image data set from ImageCLEF. Our results show that the boosting framework compares favorably to state-of-the-art approaches for distance metric learning in retrieval accuracy, with much lower computational cost. Additional evaluation with the COREL collection shows that our algorithm works well for regular image data sets. 2010-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2315 info:doi/10.1109/TPAMI.2008.273 https://ink.library.smu.edu.sg/context/sis_research/article/3315/viewcontent/A_Boosting_Framework_for_Visuality_Preserving_Dist.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Boosting Distance Metric Learning Image/video retrieval Machine learning boosting distance metric learning image retrieval Artificial Intelligence and Robotics Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Boosting
Distance Metric Learning
Image/video retrieval
Machine learning
boosting
distance metric learning
image retrieval
Artificial Intelligence and Robotics
Theory and Algorithms
spellingShingle Boosting
Distance Metric Learning
Image/video retrieval
Machine learning
boosting
distance metric learning
image retrieval
Artificial Intelligence and Robotics
Theory and Algorithms
LIU, Yang
JIN, Rong
Mummert, Lily
Sukthankar, Rahul
Goode, Adam
ZHENG, Bin
HOI, Steven C. H.
Satyanarayanan, Mahadev
A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval
description Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming distance using the learned binary representation. A boosting algorithm is presented to efficiently learn the distance function. We evaluate the proposed algorithm on a mammographic image reference library with an Interactive Search-Assisted Decision Support (ISADS) system and on the medical image data set from ImageCLEF. Our results show that the boosting framework compares favorably to state-of-the-art approaches for distance metric learning in retrieval accuracy, with much lower computational cost. Additional evaluation with the COREL collection shows that our algorithm works well for regular image data sets.
format text
author LIU, Yang
JIN, Rong
Mummert, Lily
Sukthankar, Rahul
Goode, Adam
ZHENG, Bin
HOI, Steven C. H.
Satyanarayanan, Mahadev
author_facet LIU, Yang
JIN, Rong
Mummert, Lily
Sukthankar, Rahul
Goode, Adam
ZHENG, Bin
HOI, Steven C. H.
Satyanarayanan, Mahadev
author_sort LIU, Yang
title A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval
title_short A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval
title_full A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval
title_fullStr A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval
title_full_unstemmed A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval
title_sort boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval
publisher Institutional Knowledge at Singapore Management University
publishDate 2010
url https://ink.library.smu.edu.sg/sis_research/2315
https://ink.library.smu.edu.sg/context/sis_research/article/3315/viewcontent/A_Boosting_Framework_for_Visuality_Preserving_Dist.pdf
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