Paper Info
Title
Data Compression Of Digital-Ink Using B-Spline Approach With Sparse Coding
Abstract
This paper considers a problem of highly compressing the data called "Digital-Ink" which is a sequence of position data sampled from the traced curve at a sampling rate. We here suppose that a set of digital-ink is measured and stored as two-dimensional position data by electronic device (e.g. smart-phone and pen-tablet PC, etc.). Then, we develop a method of digital-ink compression using B-spline approach with sparse coding. Such a compression method consists of two steps: (i) approximating by B-splines and (ii) sparse coding. By the step (i), the digital-ink is compressed as a control point vector which is a sequence of B-spline's control points. Then, in the step (ii), such a control point vector is shrunk to a sparse vector. In particular, employing a method of dictionary learning called 'K-SVD algorithm', we create the best dictionary so that the control point vector can be represented sparsely. We demonstrate the performance by some experimental studies.
Year
Venue
Field
2016
2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
B-spline,Control point,K-SVD,Computer science,Neural coding,Sparse approximation,Artificial intelligence,Data compression,Machine learning,Sparse matrix,Encoding (memory)
DocType
ISSN
Citations 
Conference
1062-922X
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Hiroyuki Fujioka13713.37
Hiroyuki Kano27519.05