Unsupervised co-segmentation based on a new global GMM constraint in MRF
Hongkai Yu, Min Xian, Xiaojun Qi · 2014
This paper proposes a new Markov Random Fields (MRF) optimization model for co-segmentation. The co-saliency model is incorporated into our model to make it fully unsupervised and work well for images with similar backgrounds. The Gaussian Mixture Model (GMM) based dissimilarity between foregrounds in each image and the common objects in the set is involved as a new global constraint (i.e., energy term) in our model. Finally, we introduce an alternative approximation to represent the energy function, which could be minimized by Graph Cuts iteratively. The experimental results on two datasets show that our algorithm achieves better or comparable accuracy when comparing with state-of-the-art algorithms.