대용량 이미지 데이터베이스를 위한 부분 노이즈제거를 지원하는 윤곽선 매칭 시스템 설계 및 구현
Design and Implementation of a Boundary Matching System Supporting Partial Denoising for Large Image Databases
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In this paper, we design and implement a partial denoising boundary matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform a fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI(graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client and sends the resulting images to the client. Experimental results show that our system provides many intuitive and accurate matching results.
Abstract
In this paper, we design and implement a partial denoising boundary matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform a fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI(graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client and sends the resulting images to the client. Experimental results show that our system provides many intuitive and accurate matching results.
참고문헌 (13)
- Y. -S. Moon, "Scaling-Invariant Boundary Image Matching Using Time-Series Matching Techniques", Data & Knowledge Engineering, 69(10), pp.1022~1042 (2010)
- B. -S. Kim, "Interactive Noise-Controlled Boundary Image Matching Using the Time-Series Moving Average Transform", Multimedia Tools and Applications, 72(3), pp.2543~2571 (2014)
- W. -K. Loh, "Envelope-based Boundary Image Matching for Smart Devices under Arbitrary Rotations", Multimedia Systems, 21(1), pp.29~47 (2015)
- B. -S. Kim, "Boundary Image Matching Supporting Partial Denoising Using Time-Series Matching Techniques", Multimedia Tools and Applications, 76(6), pp.8471~8496 (2017)
- N. Beckmann, "The R∗-tree : An Efficient and Robust Access Method for Points and Rectangles", Proc. of the ACM SIGMOD Int’l Conf. on Management of Data, pp.322~331 (1990)
- R. Agrawal, "Efficient Similarity Search in Sequence Databases", Proc. the 4th Int'l Conf. on Foundations of Data Organization and Algorithms, pp.69~84 (1993)
- C. Faloutsos, "Fast Subsequence Matching in Time-Series Databases", Proc. of the ACM SIGMOD Int’l Conf. on Management of Data, pp.419~429 (1994)
- S. Belongie, "Shape Matching and Object Recognition Using Shape Contexts", IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(4), pp.509~522 (2002)
- R. Datta, "Image Retrieval : Ideas, Influences, and Trends of the New Age", ACM Computing Surveys, 40(2), pp.34~94 (2008)
- M. Sonka, "Image Processing, Analysis, and Machine Vision" (2014), Cengage Learning
- D. Zhang, "Review of Shape Representation and Description Techniques", Pattern Recognition, 37(1), pp.1~19 (2004)
- S. Berchtold, "The Pyramid-Technique : Towards Breaking the Curse of Dimensionality", Proc. of the ACM SIGMOD Int’l Conf. on Management of Data, pp.142~153 (1998)
- R. Arandjelovi´c, "Three Things Everyone Should Know to Improve Object Retrieval", Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp.2911~2918 (2012)