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.
|Title of host publication||2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|Publication status||Published - 2015 Dec 9|
|Event||IEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada|
Duration: 2015 Sep 27 → 2015 Sep 30
|Name||Proceedings - International Conference on Image Processing, ICIP|
|Other||IEEE International Conference on Image Processing, ICIP 2015|
|Period||15/9/27 → 15/9/30|
Bibliographical notePublisher Copyright:
© 2015 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Signal Processing