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 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 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 much intuitive and accurate matching result.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Moon, Y.-S., Kim, B.-S., Kim, M.S., Whang, K.-Y.: Scaling-invariant boundary image matching using time-series matching techniques. Data Knowl. Eng. 69(10), 1022–1042 (2010)
Kim, B.-S., Moon, Y.-S., Choi, M.-J., Kim, J.: Interactive noise-controlled boundary image matching using the time-series moving average transform. Multimed. Tools Appl. 72, 2543–2571 (2014)
Loh, W.-K., Kim, S.-P., Hong, S.-K., Moon, Y.-S.: Envelope-based boundary image matching for smart devices under arbitrary rotations. Multimedia Syst. 21(1), 29–47 (2015)
Kim, B.-S., Moon, Y.-S., Lee, J.-G.: Boundary image matching supporting partial denoising using time-series matching techniques. Multimed. Tools Appl. 76, 8471–8496 (2017)
Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R*-tree: an efficient and robust access method for points and rectangles. In: The ACM SIGMOD International Conference on Management of Data, Atlantic City, New Jersey, pp. 322–331 (1990)
Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: The 4th International Conference on Foundations of Data Organization and Algorithms, Chicago, Illinois, pp. 69–84 (1993)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: The ACM SIGMOD International Conference on Management of Data, Minneapolis, Minnesota, pp. 419–429 (1994)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), 34–94 (2008)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Cengage Learning (2014)
Zang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recogn. 37(1), 1–19 (2004)
Berchtold, S., Bohm, C., Kriegel, H.-P.: The pyramid-technique: towards breaking the curse of dimensionality. In: The ACM SIGMOD International Conference on Management of Data, Seattle, Washington, pp. 142–153 (1998)
Arandjelovic, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: The IEEE Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, pp. 2911–2918 (2012)
Kim, B.-S., Moon, Y.-S., Kim, J.: Noise-control boundary image matching using time-series moving average transform. In: The 19th International Conference on Database and Expert Systems Applications, Turin, Italy, pp. 362–375 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kim, BS., Kim, JU. (2020). Design and Implementation of a Partial Denoising Boundary Matching System Using Indexing Techniques. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2018 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-9341-9_22
Download citation
DOI: https://doi.org/10.1007/978-981-13-9341-9_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9340-2
Online ISBN: 978-981-13-9341-9
eBook Packages: EngineeringEngineering (R0)