Interactive video segmentation using occlusion boundaries and temporally coherent superpixels

Dondera, Radu; Morariu, Vlad I.; Wang, Yulu; Davis, Larry S.

doi:10.1109/wacv.2014.6836023

Cited by 11 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Video object segmentation Prior work on video object segmentation can be broadly categorized into two types: Semi-supervised methods that require manual annotation to define what is foreground object and unsupervised methods that does segmentation completely automatically. Unsupervised techniques such as [25,48,45,55,77,80,72,23] use some prior information about the foreground objects such as distinctive motion, saliency etc.…”

Section: Related Workmentioning

confidence: 99%

Video Propagation Networks

Jampani

Gadde

Gehler

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

214

199

View full text Add to dashboard Cite

We propose a technique that propagates information forward through video data. The method is conceptually simple and can be applied to tasks that require the propagation of structured information, such as semantic labels, based on video content. We propose a Video Propagation Network that processes video frames in an adaptive manner. The model is applied online: it propagates information forward without the need to access future frames. In particular we combine two components, a temporal bilateral network for dense and video adaptive filtering, followed by a spatial network to refine features and increased flexibility. We present experiments on video object segmentation and semantic video segmentation and show increased performance comparing to the best previous task-specific methods, while having favorable runtime. Additionally we demonstrate our approach on an example regression task of color propagation in a grayscale video. arXiv:1612.05478v3 [cs.CV] 11 Apr 2017 processing: General applicability: VPNs can be used to propagate any type of information content i.e., both discrete (e.g., semantic labels) and continuous (e.g., color) information across video frames. Online propagation: The method needs no future frames and can be used for online video analysis. Long-range and image adaptive: VPNs can efficiently handle a large number of input frames and are adaptive to the video with long-range pixel connections. End-to-end trainable: VPNs can be trained end-to-end, so they can be used in other deep network architectures. Favorable runtime: VPNs have favorable runtime in comparison to many current best methods, what makes them amenable for learning with large datasets.Empirically we show that VPNs, despite being generic, perform better than published approaches on video object segmentation and semantic label propagation while being faster. VPNs can easily be integrated into sequential perframe approaches and require only a small fine-tuning step that can be performed separately.

show abstract

Section: Related Workmentioning

confidence: 99%

Video Propagation Networks

Jampani

Gadde

Gehler

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

214

199

View full text Add to dashboard Cite

show abstract

“…Therefore, our method robustly responds to many challenging scenarios such as occlusions, illumination changes, scale variations, and nonrigid motions. Secondly, unlike previous spatio-temporal graph optimization based approaches [5,23,29,1,25,28], our method handles each frame independently. Therefore, a slight mis-segmentation in the current frame does not have induce critical effects on the following frames.…”

Section: Comparative Evaluationmentioning

confidence: 99%

“…Most recent approaches [5,23,29,1,25,28] separate discriminative objects from a background by optimizing an energy equation under various pixel graph relationships. For instance, fully connected graphs have been proposed in [22] to construct a long range spatio-temporal graph structure robust to challenging situations such as occlusion.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Pixel-Level Matching for Video Object Segmentation Using Convolutional Neural Networks

Yoon

Rameau

Kim

et al. 2017

2017 IEEE International Conference on Computer Vision (ICCV)

167

View full text Add to dashboard Cite

We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN). Our network aims to distinguish the target area from the background on the basis of the pixel-level similarity between two object units. The proposed network represents a target object using features from different depth layers in order to take advantage of both the spatial details and the category-level semantic information. Furthermore, we propose a feature compression technique that drastically reduces the memory requirements while maintaining the capability of feature representation. Two-stage training (pretraining and fine-tuning) allows our network to handle any target object regardless of its category (even if the object's type does not belong to the pre-training data) or of variations in its appearance through a video sequence. Experiments on large datasets demonstrate the effectiveness of our model -against related methods -in terms of accuracy, speed, and stability. Finally, we introduce the transferability of our network to different domains, such as the infrared data domain.Recently, Convolutional Neural Networks (CNN) have been extensively applied to various vision tasks given their ability to encapsulate semantic information in the object representation task itself. In spite of this advantage, however, only a few attempts [4,20,10] have been made to solve video object segmentation problems using a CNN. arXiv:1708.05137v1 [cs.CV]

show abstract

“…The above methods segmented or matted object frame by frame, and may require additional supervision in more complex videos. The long video intervals (up to 100 frames) were considered by Dondera et al [12] on the basis of occlusion and long term spatio-temporal structure cues. Their system obtained good results quickly by running spectral clustering on superpixels.…”

Section: Trends In Engineering and Technology (Nctet-2k17) Internatiomentioning

confidence: 99%

An Effective Approach to Streaming Video Segmentation with Sub-Optimal Low Rank Decomposition

Sujatha¹,

Babu²

2017

Nctet-2k17

View full text Add to dashboard Cite

Abstract-This paper explores how to perform powerful and proficient video division while smothering the impacts of information commotions and additionally debasements, and a viable approach is acquainted with this end. Initial, a general calculation, called problematic low-rank decay (SOLD), is proposed to seek after the low-rank portrayal for video division. Given the information framework shaped by supervoxel elements of a watched video succession, SOLD looks for a problematic arrangement by making the lattice rank unequivocally decided. Specifically, the portrayal coefficient grid with the settled rank can be disintegrated into two sub-networks of low rank, and after that we iteratively streamline them with shut frame arrangements. Additionally, we join a discriminative replication earlier into SOLD in light of the perception that little size video designs have a tendency to repeat regularly inside a similar question. Second, in light of SOLD, we introduce an effective deduction calculation to perform gushing video division in both unsupervised and intuitive situations. All the more particularly, the obliged standardized cut calculation is embraced by joining the low-rank portrayal with other low level signs and fleeting reliable imperatives for spatiotemporal division. Broad analyses on two open testing informational collections VSB100 and Seg Track propose that our approach beats other video division approaches in both exactness and effectiveness.

show abstract

Interactive video segmentation using occlusion boundaries and temporally coherent superpixels

Cited by 11 publications

References 18 publications

Video Propagation Networks

Video Propagation Networks

Pixel-Level Matching for Video Object Segmentation Using Convolutional Neural Networks

An Effective Approach to Streaming Video Segmentation with Sub-Optimal Low Rank Decomposition

Contact Info

Product

Resources

About