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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol. 3(1): 147-150 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.290

Lossless Location Coding for Image Feature Points

Gyeongmin Choi, Hyunil Jung, and Haekwang Kim
Abstract—Image retrieval research activity has moved its focus from global descriptors to local descriptors of feature point such as SIFT. Currently MPEG is working on standardization of effective coding of location and local descriptors of feature point in the context mobile based image search driven application in the name of MPEG-7 CDVS (Compact Descriptor for Visual Search). While CDVS is dealing with lossy compression of location information, this paper presents various lossless compression methods of locations and provides comparative experimental results for applications requiring fine matching precision. Among 4 methods presented in this paper, Experimental results show that Block-based lossless location coding using circular scan order method shows the best compression results with compression ratio achieving 2.5 to 1 with reference to Fixed-Length lossless location coding method.

Index Terms—Local Feature, location coding, compression, lossless

The authors are with the Department of Computer Engineering, Sejong University, Korea (e-mail: hkkim@sejong.ac.kr).


Cite:Gyeongmin Choi, Hyunil Jung, and Haekwang Kim, "Lossless Location Coding for Image Feature Points," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 147-150, 2013.

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