A pyramid matching framework for near duplicate image retrieval

There is a growing interest in Content-based Image Retrieval (CBIR) due to the range of its potential uses, as well as the inherent limitations of the traditional metadata- based system. The most common technique for comparing two images in CBIR is to introduce a distance measure, which compares the...

Full description

Saved in:
Bibliographic Details
Main Author: Niu, Yiming.
Other Authors: Xu Dong
Format: Final Year Project
Language:English
Published: 2011
Subjects:
Online Access:http://hdl.handle.net/10356/43858
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-43858
record_format dspace
spelling sg-ntu-dr.10356-438582023-03-03T20:52:05Z A pyramid matching framework for near duplicate image retrieval Niu, Yiming. Xu Dong School of Computer Engineering Centre for Multimedia and Network Technology DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition There is a growing interest in Content-based Image Retrieval (CBIR) due to the range of its potential uses, as well as the inherent limitations of the traditional metadata- based system. The most common technique for comparing two images in CBIR is to introduce a distance measure, which compares their similarities in various dimensions such as color, texture, shape, and others. A positive value implies some degree of dissimilarities between these images. The task of CBIR can therefore be classified as a ranking problem. Many CBIR systems have been developed so far. However, it remains a challenging task for most of them to cope with the spatial shifts and scale variations among testing images. A recently proposed framework, termed Spatially Aligned Pyramid Matching (SAPM) by Xu Dong et al., reveals its unmatched robustness against these problems in empirical observations. The author then launched a project to explore the nature of this method and investigate the appropriate configurations, by applying which the optimal tradeoff between the performance and speed of SAPM could be achieved. More than conducting extensive experiments on Xu’s original work, the author came up with some revised algorithms that improved both of its retrieval accuracy and efficiency to a certain extent. It was noticed that this system can report up to 90% correct matching results on two prescribed image databases, within a quite acceptable wait time. To allow reviewers to perceptually understand the advantages of SAPM, a Matlab GUI-based demo system was implemented. Users are free to switch among the kernel frameworks and make necessary settings for each. Bachelor of Engineering (Computer Engineering) 2011-05-04T03:08:35Z 2011-05-04T03:08:35Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/43858 en Nanyang Technological University 55 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Niu, Yiming.
A pyramid matching framework for near duplicate image retrieval
description There is a growing interest in Content-based Image Retrieval (CBIR) due to the range of its potential uses, as well as the inherent limitations of the traditional metadata- based system. The most common technique for comparing two images in CBIR is to introduce a distance measure, which compares their similarities in various dimensions such as color, texture, shape, and others. A positive value implies some degree of dissimilarities between these images. The task of CBIR can therefore be classified as a ranking problem. Many CBIR systems have been developed so far. However, it remains a challenging task for most of them to cope with the spatial shifts and scale variations among testing images. A recently proposed framework, termed Spatially Aligned Pyramid Matching (SAPM) by Xu Dong et al., reveals its unmatched robustness against these problems in empirical observations. The author then launched a project to explore the nature of this method and investigate the appropriate configurations, by applying which the optimal tradeoff between the performance and speed of SAPM could be achieved. More than conducting extensive experiments on Xu’s original work, the author came up with some revised algorithms that improved both of its retrieval accuracy and efficiency to a certain extent. It was noticed that this system can report up to 90% correct matching results on two prescribed image databases, within a quite acceptable wait time. To allow reviewers to perceptually understand the advantages of SAPM, a Matlab GUI-based demo system was implemented. Users are free to switch among the kernel frameworks and make necessary settings for each.
author2 Xu Dong
author_facet Xu Dong
Niu, Yiming.
format Final Year Project
author Niu, Yiming.
author_sort Niu, Yiming.
title A pyramid matching framework for near duplicate image retrieval
title_short A pyramid matching framework for near duplicate image retrieval
title_full A pyramid matching framework for near duplicate image retrieval
title_fullStr A pyramid matching framework for near duplicate image retrieval
title_full_unstemmed A pyramid matching framework for near duplicate image retrieval
title_sort pyramid matching framework for near duplicate image retrieval
publishDate 2011
url http://hdl.handle.net/10356/43858
_version_ 1759853488218570752