In digital storage media, Video segmentation is one of the popular and important techniques.The challenging problem in digital storage media is video segmentation. In this hybrid video segmentation technique, initially similar shots are segmented. Then subsequently the track frames in every shot are assorted using the extracted objects of every frame which highly mostly reduces the processing t...

Mixture models for video segmentation have mainly revolved around Gaussian distributions for a long time due to their simplicity and applicability. In this work, we propose a novel real-time video segmentation algorithm based on Student’s t mixture model. Though, Student’s t-distribution has been used for image segmentation by applying Expectation Maximization (EM) algorithm, the same technique...

The problem of figure–ground segmentation is of great importance in both video editing and visual perception tasks. Classical video segmentation algorithms approach the problem from one of two perspectives. At one extreme, global approaches constrain the camera motion to simplify the image structure. At the other extreme, local approaches estimate motion in small image regions over a small numb...

We apply the fast bilateral solver technique to the problem of real-time semantic video segmentation. While structured prediction by a dense CRF is accurate on video datasets, the performance is not adequate for real-time segmentation. We hope to utilize the efficient smoothing methodology from the fast bilateral solver within the video segmentation framework introduced by Kundu et al. [9], imp...

This paper introduces a novel automatic video object extraction algorithm based on combination of color and motion segmentation results. The algorithm includes five parts: preprocessing, color segmentation, motion segmentation, combination of color and motion segmentation of multiple frames, post-processing. The performance of this algorithm is very promising, resulting in pixel-wise accuracy o...

In this paper, we introduce the robust recursive GPCA algorithm that can segment a set of data points into multiple unknown linear models with different dimensions in the presence of noise and outliers. The algorithm is designed based on a robust model selection criterion for mixtures of subspaces called minimum effective dimension (MED). We apply the robust GPCA algorithm in a video segmentati...