Sparse edit propagation for high resolution image using support vector machines

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.