Perfect Snapping

Qingsong Zhu, Ling Shao, Qi Li, Yaoqin Xie

Research output: Contribution to conferencePaper

Abstract

Interactive image matting is a process that extracts a foreground object from an image based on limited user input. In this paper, we propose a novel interactive image matting algorithm named Perfect Snapping which is inspired by the presented method named Lazy Snapping technique. In the algorithm, the mean shift algorithm with a boundary confidence prior is introduced to efficiently pre-segment the original image into homogeneous regions (super-pixels) with precise boundary. Secondly, Gaussian Mixture Model (GMM) clustering algorithm is used to describe and to model the super-pixels. Finally, a Monte Carlo based Expectation Maximization (EM) algorithm is used to perform parametric learning of mixture model for priori knowledge. Experimental results indicate that the proposed algorithm can achieve higher matting quality with higher efficiency.
Original languageEnglish
DOIs
Publication statusPublished - Jan 2013
EventMMM 2013 - 19th International Conference on Multimedia Modelling - Huangshang, China
Duration: 1 Jan 2013 → …

Conference

ConferenceMMM 2013 - 19th International Conference on Multimedia Modelling
Period1/01/13 → …

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