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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제17권 제5호
- 발행연도
- 2021.10
- 수록면
- 1,020 - 1,033 (14page)
- DOI
- 10.3745/JIPS.02.0167
이용수
초록· 키워드
오류제보하기
Nowadays, online or mobile social network services (SNS) are very popular and widely spread in our society
and daily lives to instantly share, disseminate, and search information. In particular, SNS such as YouTube,
Flickr, Facebook, and Amazon allow users to upload billions of images or videos and also provide a number of
multimedia information to users. Information retrieval in multimedia-rich SNS is very useful but challenging
task. Content-based media retrieval (CBMR) is the process of obtaining the relevant image or video objects for
a given query from a collection of information sources. However, CBMR suffers from the dimensionality curse
due to inherent high dimensionality features of media data. This paper investigates the effectiveness of the
kernel trick in CBMR, specifically, the kernel principal component analysis (KPCA) for dimensionality reduction.
KPCA is a nonlinear extension of linear principal component analysis (LPCA) to discovering nonlinear
embeddings using the kernel trick. The fundamental idea of KPCA is mapping the input data into a highdimensional
feature space through a nonlinear kernel function and then computing the principal components
on that mapped space. This paper investigates the potential of KPCA in CBMR for feature extraction or dimensionality
reduction. Using the Gaussian kernel in our experiments, we compute the principal components of an
image dataset in the transformed space and then we use them as new feature dimensions for the image dataset.
Moreover, KPCA can be applied to other many domains including CBMR, where LPCA has been used to
extract features and where the nonlinear extension would be effective. Our results from extensive experiments
demonstrate that the potential of KPCA is very encouraging compared with LPCA in CBMR.
목차
등록된 정보가 없습니다.
참고문헌 (23)
참고문헌 신청
W. Ahmad / 2016 / Information retrieval from social networks: a survey / Proceedings of 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), :631~635
N. Beckmann / 1990 / The R*-tree: an efficient and robust access method for points and rectangles / Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data :322~331
A. W. C. Fu / 2000 / Dynamic VP-tree indexing for n-nearest neighbor search given pair-wise distances / The VLDB Journal 9(2):154~173
이상곤 / 2013 / An Improvement Video Search Method for VP-Tree by using a Trigonometric Inequality / JIPS(Journal of Information Processing Systems) 9(2):315~332
I. Kamel / 1994 / Hilbert R-tree: an improved R-tree using fractals / Proceedings of 20th International Conference on Very Large Data Bases :500~509
N. Beckmann / 1990 / The R*-tree: an efficient and robust access method for points and rectangles / Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data :322~331
A. W. C. Fu / 2000 / Dynamic VP-tree indexing for n-nearest neighbor search given pair-wise distances / The VLDB Journal 9(2):154~173
이상곤 / 2013 / An Improvement Video Search Method for VP-Tree by using a Trigonometric Inequality / JIPS(Journal of Information Processing Systems) 9(2):315~332
I. Kamel / 1994 / Hilbert R-tree: an improved R-tree using fractals / Proceedings of 20th International Conference on Very Large Data Bases :500~509
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