Images information retrieval using Gustafson-Kessel relevance feedback

The goal of CBIR is to retrieve images that are visually similar to the query image. Relevance feedback retrieval systems ask the user for feedback on retrieval results and then use this feedback on later retrievals with the goal of increasing retrieval performance. The objectives of this research a...

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Yazar: Zainuddin, Nurulhuda
Materyal Türü: Tez
Dil:English
Baskı/Yayın Bilgisi: 2006
Konular:
Online Erişim:http://eprints.utm.my/5383/1/NurulhudaZainuddinMFSKSM2006.pdf
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author Zainuddin, Nurulhuda
author_facet Zainuddin, Nurulhuda
author_sort Zainuddin, Nurulhuda
collection ePrints
description The goal of CBIR is to retrieve images that are visually similar to the query image. Relevance feedback retrieval systems ask the user for feedback on retrieval results and then use this feedback on later retrievals with the goal of increasing retrieval performance. The objectives of this research are to compare CBIR based with Gustafson-Kessel (GK) clustering and relevant feedback approach and to evaluate the effectiveness of GK relevance feedback for images retrieval. The research requires a better understanding of GK clustering and probabilistic relevance feedback method in turn to figure out different methods that can be used in solving similar problems. This project will give better insights in the usage of relevance feedback learning in order to reduce the gap between low-level features and high-level human concepts. The research will evaluate Gustafson-Kessel clustering and probabilistic relevance feedback method to improve the retrieval performance.
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spelling utm.eprints-53832018-03-07T20:59:28Z http://eprints.utm.my/5383/ Images information retrieval using Gustafson-Kessel relevance feedback Zainuddin, Nurulhuda QA75 Electronic computers. Computer science The goal of CBIR is to retrieve images that are visually similar to the query image. Relevance feedback retrieval systems ask the user for feedback on retrieval results and then use this feedback on later retrievals with the goal of increasing retrieval performance. The objectives of this research are to compare CBIR based with Gustafson-Kessel (GK) clustering and relevant feedback approach and to evaluate the effectiveness of GK relevance feedback for images retrieval. The research requires a better understanding of GK clustering and probabilistic relevance feedback method in turn to figure out different methods that can be used in solving similar problems. This project will give better insights in the usage of relevance feedback learning in order to reduce the gap between low-level features and high-level human concepts. The research will evaluate Gustafson-Kessel clustering and probabilistic relevance feedback method to improve the retrieval performance. 2006-06 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/5383/1/NurulhudaZainuddinMFSKSM2006.pdf Zainuddin, Nurulhuda (2006) Images information retrieval using Gustafson-Kessel relevance feedback. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System.
spellingShingle QA75 Electronic computers. Computer science
Zainuddin, Nurulhuda
Images information retrieval using Gustafson-Kessel relevance feedback
title Images information retrieval using Gustafson-Kessel relevance feedback
title_full Images information retrieval using Gustafson-Kessel relevance feedback
title_fullStr Images information retrieval using Gustafson-Kessel relevance feedback
title_full_unstemmed Images information retrieval using Gustafson-Kessel relevance feedback
title_short Images information retrieval using Gustafson-Kessel relevance feedback
title_sort images information retrieval using gustafson kessel relevance feedback
topic QA75 Electronic computers. Computer science
url http://eprints.utm.my/5383/1/NurulhudaZainuddinMFSKSM2006.pdf
work_keys_str_mv AT zainuddinnurulhuda imagesinformationretrievalusinggustafsonkesselrelevancefeedback