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공공누리This item is licensed Korea Open Government License

Title
Video learning based image classification method for object recognition
Author(s)
이홍로신용주
Publication Year
2013-09-01
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.
Keyword
image classification; multi-viewpoint image; feature extraction; video learning
Journal Title
Journal of Central South University
ISSN
2095-2899
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Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
URI
https://repository.kisti.re.kr/handle/10580/14176
http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NART70433936
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