Accelerating of Image Retrieval in CBIR System with Relevance Feedback
<p/> <p>Content-based image retrieval (CBIR) system with relevance feedback, which uses the algorithm for feature-vector (FV) dimension reduction, is described. Feature-vector reduction (FVR) exploits the clustering of FV components for a given query. Clustering is based on the compariso...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2007-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://asp.eurasipjournals.com/content/2007/062678 |
| _version_ | 1811266763552194560 |
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| author | Radosavljević Vladan Rudinac Stevan Reljin Branimir Zajić Goran Kojić Nenad Rudinac Maja Reljin Nikola Reljin Irini |
| author_facet | Radosavljević Vladan Rudinac Stevan Reljin Branimir Zajić Goran Kojić Nenad Rudinac Maja Reljin Nikola Reljin Irini |
| author_sort | Radosavljević Vladan |
| collection | DOAJ |
| description | <p/> <p>Content-based image retrieval (CBIR) system with relevance feedback, which uses the algorithm for feature-vector (FV) dimension reduction, is described. Feature-vector reduction (FVR) exploits the clustering of FV components for a given query. Clustering is based on the comparison of magnitudes of FV components of a query. Instead of all FV components describing color, line directions, and texture, only their representative members describing FV clusters are used for retrieval. In this way, the "curse of dimensionality" is bypassed since redundant components of a query FV are rejected. It was shown that about one tenth of total FV components (i.e., the reduction of 90%) is sufficient for retrieval, without significant degradation of accuracy. Consequently, the retrieving process is accelerated. Moreover, even better balancing between color and line/texture features is obtained. The efficiency of FVR CBIR system was tested over TRECVid 2006 and Corel 60 K datasets.</p> |
| first_indexed | 2024-04-12T20:49:11Z |
| format | Article |
| id | doaj.art-69e15d5a37434949870ce188199dc1e3 |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-04-12T20:49:11Z |
| publishDate | 2007-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-69e15d5a37434949870ce188199dc1e32022-12-22T03:17:10ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802007-01-0120071062678Accelerating of Image Retrieval in CBIR System with Relevance FeedbackRadosavljević VladanRudinac StevanReljin BranimirZajić GoranKojić NenadRudinac MajaReljin NikolaReljin Irini<p/> <p>Content-based image retrieval (CBIR) system with relevance feedback, which uses the algorithm for feature-vector (FV) dimension reduction, is described. Feature-vector reduction (FVR) exploits the clustering of FV components for a given query. Clustering is based on the comparison of magnitudes of FV components of a query. Instead of all FV components describing color, line directions, and texture, only their representative members describing FV clusters are used for retrieval. In this way, the "curse of dimensionality" is bypassed since redundant components of a query FV are rejected. It was shown that about one tenth of total FV components (i.e., the reduction of 90%) is sufficient for retrieval, without significant degradation of accuracy. Consequently, the retrieving process is accelerated. Moreover, even better balancing between color and line/texture features is obtained. The efficiency of FVR CBIR system was tested over TRECVid 2006 and Corel 60 K datasets.</p>http://asp.eurasipjournals.com/content/2007/062678 |
| spellingShingle | Radosavljević Vladan Rudinac Stevan Reljin Branimir Zajić Goran Kojić Nenad Rudinac Maja Reljin Nikola Reljin Irini Accelerating of Image Retrieval in CBIR System with Relevance Feedback EURASIP Journal on Advances in Signal Processing |
| title | Accelerating of Image Retrieval in CBIR System with Relevance Feedback |
| title_full | Accelerating of Image Retrieval in CBIR System with Relevance Feedback |
| title_fullStr | Accelerating of Image Retrieval in CBIR System with Relevance Feedback |
| title_full_unstemmed | Accelerating of Image Retrieval in CBIR System with Relevance Feedback |
| title_short | Accelerating of Image Retrieval in CBIR System with Relevance Feedback |
| title_sort | accelerating of image retrieval in cbir system with relevance feedback |
| url | http://asp.eurasipjournals.com/content/2007/062678 |
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