JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
The Kernel Trick for Content-Based Media Retrieval in Online Social Networks |
| 영문제목(English Title) |
The Kernel Trick for Content-Based Media Retrieval in Online Social Networks |
| 저자(Author) |
Guang-Ho Cha
|
| 원문수록처(Citation) |
VOL 17 NO. 05 PP. 1020 ~ 1033 (2021. 10) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
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 high dimensional 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. |
| 키워드(Keyword) |
Content-Based Retrieval
Dimensionality Curse
Nearest Neighbor Query
Online Social Network
Kernel Method
Kernel Principal Component Analysis
Similarity Search
Social Network Service
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