TY - GEN
T1 - Sparse edit propagation for high resolution image using support vector machines
AU - Oh, Changjae
AU - Ryu, Seungchul
AU - Kim, Youngjung
AU - Kim, Jihyun
AU - Park, Taewoong
AU - Sohn, Kwanghoon
N1 - Publisher Copyright:
© 2015 IEEE.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2015/12/9
Y1 - 2015/12/9
N2 - In this paper, we formulate image edit propagation as a task of machine learning to handle a high resolution image efficiently. Conventional graph-based methods solve the edit propagation by minimizing an energy function which considers the relationship between a reference pixel and its spatially neighboring ones. It is becoming a time-consuming and memory-requiring task due to the increase of the image size. Inspired by the observation that similar features get analogous edits, the edit propagation is casted as a classification problem using support vector machines in the feature space. A classifier is trained with initial sparse edits given by user interaction, and then the rest of the features are classified and manipulated. In experiments, the proposed method is applied to an image recoloring to verify the performance. Experimental results show that the proposed method gives competitive editing results comparing to other state-of-the-art methods.
AB - In this paper, we formulate image edit propagation as a task of machine learning to handle a high resolution image efficiently. Conventional graph-based methods solve the edit propagation by minimizing an energy function which considers the relationship between a reference pixel and its spatially neighboring ones. It is becoming a time-consuming and memory-requiring task due to the increase of the image size. Inspired by the observation that similar features get analogous edits, the edit propagation is casted as a classification problem using support vector machines in the feature space. A classifier is trained with initial sparse edits given by user interaction, and then the rest of the features are classified and manipulated. In experiments, the proposed method is applied to an image recoloring to verify the performance. Experimental results show that the proposed method gives competitive editing results comparing to other state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=84956669732&partnerID=8YFLogxK
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U2 - 10.1109/ICIP.2015.7351565
DO - 10.1109/ICIP.2015.7351565
M3 - Conference contribution
AN - SCOPUS:84956669732
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 4042
EP - 4046
BT - 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PB - IEEE Computer Society
T2 - IEEE International Conference on Image Processing, ICIP 2015
Y2 - 27 September 2015 through 30 September 2015
ER -