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공공누리This item is licensed Korea Open Government License

dc.contributor.author
이홍로
dc.contributor.author
신용주
dc.date.accessioned
2019-08-28T07:41:21Z
dc.date.available
2019-08-28T07:41:21Z
dc.date.issued
2013-09-01
dc.identifier.issn
2095-2899
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/14176
dc.identifier.uri
http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NART70433936
dc.description.abstract
Automatic image classification is the first step toward semantic understanding of an object in the computer vision area. The key challenge of problem for accurate object recognition is the ability to extract the robust features from various viewpoint images and rapidly calculate similarity between features in the image database or video stream. In order to solve these problems, an effective and rapid image classification method was presented for the object recognition based on the video learning technique. The opticalflow and RANSAC algorithm were used to acquire scene images from each video sequence. After the selection of scene images, the local maximum points on corner of object around local area were found using the Harris corner detection algorithm and the several attributes from local block around each feature point were calculated by using scale invariant feature transform (SIFT) for extracting local descriptor. Finally, the extracted local descriptor was learned to the threedimensional pyramid match kernel. Experimental results show that our method can extract features.
dc.language
eng
dc.relation.ispartofseries
Journal of Central South University
dc.title
Video learning based image classification method for object recognition
dc.subject.keyword
image classification
dc.subject.keyword
multi-viewpoint image
dc.subject.keyword
feature extraction
dc.subject.keyword
video learning
Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
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