Mean shift-based SIFT keypoint filtering for region-of-interest determination

Ji Soo Keum, Hyon Soo Lee, Masafumi Hagiwara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents an improved keypoint filtering method for region-of-interest (ROI) determination. Mean shift-based clustering was employed to group the scale invariant feature transform (SIFT) keypoints that appeared in the nearest region to get more locality. The proposed method uses the location of the extracted SIFT keypoints for grouping, and an average SIFT descriptor is calculated on the clustered keypoints. The support vector machine (SVM) classifies the average SIFT descriptor as an artificial or a natural keypoint. After the keypoint classification, only the keypoints classified as artificial keypoints by the binary SVM are used in near-duplicate detection (NDD). Finally, we determine the ROI using the adaptive selection of orientation histogram and the elimination of isolated keypoints. According to the result of experiments on keypoint classification, NDD and ROI determination, the proposed method obtained improved results compared to the previous methods.

Original languageEnglish
Title of host publication6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
Pages266-271
Number of pages6
DOIs
Publication statusPublished - 2012
Event2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012 - Kobe, Japan
Duration: 2012 Nov 202012 Nov 24

Other

Other2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012
CountryJapan
CityKobe
Period12/11/2012/11/24

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Support vector machines
Experiments

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Keum, J. S., Lee, H. S., & Hagiwara, M. (2012). Mean shift-based SIFT keypoint filtering for region-of-interest determination. In 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012 (pp. 266-271). [6505144] https://doi.org/10.1109/SCIS-ISIS.2012.6505144

Mean shift-based SIFT keypoint filtering for region-of-interest determination. / Keum, Ji Soo; Lee, Hyon Soo; Hagiwara, Masafumi.

6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012. 2012. p. 266-271 6505144.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Keum, JS, Lee, HS & Hagiwara, M 2012, Mean shift-based SIFT keypoint filtering for region-of-interest determination. in 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012., 6505144, pp. 266-271, 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012, Kobe, Japan, 12/11/20. https://doi.org/10.1109/SCIS-ISIS.2012.6505144
Keum JS, Lee HS, Hagiwara M. Mean shift-based SIFT keypoint filtering for region-of-interest determination. In 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012. 2012. p. 266-271. 6505144 https://doi.org/10.1109/SCIS-ISIS.2012.6505144
Keum, Ji Soo ; Lee, Hyon Soo ; Hagiwara, Masafumi. / Mean shift-based SIFT keypoint filtering for region-of-interest determination. 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012. 2012. pp. 266-271
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