We present a novel computational model for simultaneous image co-saliency detection and
co-segmentation that concurrently explores the concepts of saliency and objectness in …

Segmenting the foreground objects from an image is an essential low-level step for many
expert and intelligent systems, and the success of this key process largely depends on the …

It is widely acknowledged that object segmentation is a significant research field for
computer vision and a key process for many other visual tasks. In the past unsupervised …

Segmenting organisms or tumors from medical data (eg, computed tomography volumetric
images, ultrasound, or magnetic resonance imaging images/image sequences) is one of the …

The correlation between images is crucial for solving the image co-segmentation problem
that is segmenting common and salient objects from a set of related images. This paper …

The existing cosegmentation methods focus on exploiting inter-image information to extract
a common object from a single image group. Observing that in many practical scenarios …

The problem of unsupervised segmentation of multi-class regions can be significantly
boosted when they irregularly recur in multiple images. The existing segmentation methods …

In this paper, we propose a novel feature adaptive co-segmentation method that can learn
adaptive features of different image groups for accurate common objects segmentation. We …

This paper proposes a new constrained directed graph clustering (DGC) method and
segmentation propagation method for the multiple foreground cosegmentation. We solve the …

Object part segmentation is an important and challenging task in computer vision. The
existing supervised part segmentation methods need pixel level training data which leads to …