Sparse edit propagation for high resolution image using support vector machines

Changjae Oh, Seungchul Ryu, Youngjung Kim, Jihyun Kim, Taewoong Park, Kwanghoon Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages4042-4046
Number of pages5
ISBN (Electronic)9781479983391
DOIs
Publication statusPublished - 2015 Dec 9
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 2015 Sep 272015 Sep 30

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing, ICIP 2015
CountryCanada
CityQuebec City
Period15/9/2715/9/30

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All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Oh, C., Ryu, S., Kim, Y., Kim, J., Park, T., & Sohn, K. (2015). Sparse edit propagation for high resolution image using support vector machines. In 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings (pp. 4042-4046). [7351565] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2015-December). IEEE Computer Society. https://doi.org/10.1109/ICIP.2015.7351565