Despite the progress of interactive image segmentation methods, high-quality pixel-level annotation is still time-consuming and laborious - a bottleneck for several deep learning applications. We take step back to propose simultaneous segment from multiple images guided by feature space projection. This strategy in stark contrast existing methodologies, which perform domain. show that achieves ...

Dataset augmentation, the practice of applying a wide array of domain-specific transformations to synthetically expand a training set, is a standard tool in supervised learning. While effective in tasks such as visual recognition, the set of transformations must be carefully designed, implemented, and tested for every new domain, limiting its re-use and generality. In this paper, we adopt a sim...

This paper presents a family of methods for data translation in feature space, to be used in conjunction with kernel machines. The translations are performed using only kernel evaluations in input space. We use the methods to improve the numerical properties of kernel machines. Experiments with synthetic and real data demonstrate the effectiveness of data centering and highlight other interesti...

In this paper we propose the Possibilistic C-Means in Feature Space and the One-Cluster Possibilistic C-Means in Feature Space algorithms which are kernel methods for clustering in feature space based on the possibilistic approach to clustering. The proposed algorithms retain the properties of the possibilistic clustering, working as density estimators in feature space and showing high robustne...