Comparative Analysis of Keypoint Detection Performance in SIFT Implementations on Small-Scale Image Datasets
Scale-invariant feature transform (SIFT) is widely used as an image local feature extraction method because of its invariance to rotation, scale, and illumination change. SIFT has been implemented in different program libraries. However, studies that analyze the performance of SIFT implementations h...
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| Format: | Article |
| Language: | English |
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Pusat Penelitian dan Pengabdian Masyarakat (P3M), Politeknik Negeri Cilacap
2024-12-01
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| Series: | Journal of Innovation Information Technology and Application |
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| Online Access: | https://ejournal.pnc.ac.id/index.php/jinita/article/view/2399 |
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| _version_ | 1850094692750852096 |
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| author | Arif Rahman Suprihatin Imam Riadi Tawar Furizal |
| author_facet | Arif Rahman Suprihatin Imam Riadi Tawar Furizal |
| author_sort | Arif Rahman |
| collection | DOAJ |
| description | Scale-invariant feature transform (SIFT) is widely used as an image local feature extraction method because of its invariance to rotation, scale, and illumination change. SIFT has been implemented in different program libraries. However, studies that analyze the performance of SIFT implementations have not been conducted. This study examines the keypoint extraction of three well-known SIFT libraries, i.e., David Lowe's implementation, OpenSIFT, and vlSIFT in vlfeat. Performance analysis was conducted on multiclass small-scale image datasets to capture the sensitivity of keypoint detection. Although libraries are based on the same algorithm, their performance differs slightly. Regarding execution time and the average number of keypoints detected in each image, vlSIFT outperforms David Lowe’s library and OpenSIFT. |
| format | Article |
| id | doaj-art-81a6a8d51cb5404cb930a97a33c68c17 |
| institution | DOAJ |
| issn | 2716-0858 2715-9248 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Pusat Penelitian dan Pengabdian Masyarakat (P3M), Politeknik Negeri Cilacap |
| record_format | Article |
| series | Journal of Innovation Information Technology and Application |
| spelling | doaj-art-81a6a8d51cb5404cb930a97a33c68c172025-08-20T02:41:36ZengPusat Penelitian dan Pengabdian Masyarakat (P3M), Politeknik Negeri CilacapJournal of Innovation Information Technology and Application2716-08582715-92482024-12-01629210010.35970/jinita.v6i2.23992399Comparative Analysis of Keypoint Detection Performance in SIFT Implementations on Small-Scale Image DatasetsArif Rahman0SuprihatinImam RiadiTawarFurizalUADScale-invariant feature transform (SIFT) is widely used as an image local feature extraction method because of its invariance to rotation, scale, and illumination change. SIFT has been implemented in different program libraries. However, studies that analyze the performance of SIFT implementations have not been conducted. This study examines the keypoint extraction of three well-known SIFT libraries, i.e., David Lowe's implementation, OpenSIFT, and vlSIFT in vlfeat. Performance analysis was conducted on multiclass small-scale image datasets to capture the sensitivity of keypoint detection. Although libraries are based on the same algorithm, their performance differs slightly. Regarding execution time and the average number of keypoints detected in each image, vlSIFT outperforms David Lowe’s library and OpenSIFT.https://ejournal.pnc.ac.id/index.php/jinita/article/view/2399siftkeypointimageopensiftvlfeat |
| spellingShingle | Arif Rahman Suprihatin Imam Riadi Tawar Furizal Comparative Analysis of Keypoint Detection Performance in SIFT Implementations on Small-Scale Image Datasets Journal of Innovation Information Technology and Application sift keypoint image opensift vlfeat |
| title | Comparative Analysis of Keypoint Detection Performance in SIFT Implementations on Small-Scale Image Datasets |
| title_full | Comparative Analysis of Keypoint Detection Performance in SIFT Implementations on Small-Scale Image Datasets |
| title_fullStr | Comparative Analysis of Keypoint Detection Performance in SIFT Implementations on Small-Scale Image Datasets |
| title_full_unstemmed | Comparative Analysis of Keypoint Detection Performance in SIFT Implementations on Small-Scale Image Datasets |
| title_short | Comparative Analysis of Keypoint Detection Performance in SIFT Implementations on Small-Scale Image Datasets |
| title_sort | comparative analysis of keypoint detection performance in sift implementations on small scale image datasets |
| topic | sift keypoint image opensift vlfeat |
| url | https://ejournal.pnc.ac.id/index.php/jinita/article/view/2399 |
| work_keys_str_mv | AT arifrahman comparativeanalysisofkeypointdetectionperformanceinsiftimplementationsonsmallscaleimagedatasets AT suprihatin comparativeanalysisofkeypointdetectionperformanceinsiftimplementationsonsmallscaleimagedatasets AT imamriadi comparativeanalysisofkeypointdetectionperformanceinsiftimplementationsonsmallscaleimagedatasets AT tawar comparativeanalysisofkeypointdetectionperformanceinsiftimplementationsonsmallscaleimagedatasets AT furizal comparativeanalysisofkeypointdetectionperformanceinsiftimplementationsonsmallscaleimagedatasets |