Incorporating SIFT with hard C-means algorithm

The scale invariant feature transform (SIFT) has been used widely as a tool in object recognition. However, when there are several keyframes for one object in the training database, the number of keypoint descriptors for that object might be huge. The matching process of a test keypoint has to be do...

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Main Authors: Wattanapong Suttapak, Sansanee Auephanwiriyakul, Nipon Theera-Umpon
Format: Conference Proceeding
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/50728
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-507282018-09-04T04:46:08Z Incorporating SIFT with hard C-means algorithm Wattanapong Suttapak Sansanee Auephanwiriyakul Nipon Theera-Umpon Computer Science Engineering The scale invariant feature transform (SIFT) has been used widely as a tool in object recognition. However, when there are several keyframes for one object in the training database, the number of keypoint descriptors for that object might be huge. The matching process of a test keypoint has to be done on all keypoints in the training database, hence, the amount of matching time is huge. Since the keyframes in the training database are from the same object, there must be some keypoints that are similar. In this paper we incorporate SIFT with the Hard C-Means (HCM) algorithm to group keypoint descriptors and then utilize the prototypes in the matching process instead. We implement this algorithm with three data sets, i.e., bottle, MPEG 7 and Thai hand gesture. We found that on the bottle and MPEG 7 test data sets, the algorithm outperform the one with SIFT with much smaller matching computation time. For the Thai hand gesture data set, the correct classification with much less matching times on the test data set from the proposed algorithm is comparable with that of SIFT. ©2010 IEEE. 2018-09-04T04:44:47Z 2018-09-04T04:44:47Z 2010-05-28 Conference Proceeding 2-s2.0-77952635713 10.1109/ICCAE.2010.5451634 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77952635713&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/50728
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
Engineering
spellingShingle Computer Science
Engineering
Wattanapong Suttapak
Sansanee Auephanwiriyakul
Nipon Theera-Umpon
Incorporating SIFT with hard C-means algorithm
description The scale invariant feature transform (SIFT) has been used widely as a tool in object recognition. However, when there are several keyframes for one object in the training database, the number of keypoint descriptors for that object might be huge. The matching process of a test keypoint has to be done on all keypoints in the training database, hence, the amount of matching time is huge. Since the keyframes in the training database are from the same object, there must be some keypoints that are similar. In this paper we incorporate SIFT with the Hard C-Means (HCM) algorithm to group keypoint descriptors and then utilize the prototypes in the matching process instead. We implement this algorithm with three data sets, i.e., bottle, MPEG 7 and Thai hand gesture. We found that on the bottle and MPEG 7 test data sets, the algorithm outperform the one with SIFT with much smaller matching computation time. For the Thai hand gesture data set, the correct classification with much less matching times on the test data set from the proposed algorithm is comparable with that of SIFT. ©2010 IEEE.
format Conference Proceeding
author Wattanapong Suttapak
Sansanee Auephanwiriyakul
Nipon Theera-Umpon
author_facet Wattanapong Suttapak
Sansanee Auephanwiriyakul
Nipon Theera-Umpon
author_sort Wattanapong Suttapak
title Incorporating SIFT with hard C-means algorithm
title_short Incorporating SIFT with hard C-means algorithm
title_full Incorporating SIFT with hard C-means algorithm
title_fullStr Incorporating SIFT with hard C-means algorithm
title_full_unstemmed Incorporating SIFT with hard C-means algorithm
title_sort incorporating sift with hard c-means algorithm
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77952635713&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/50728
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