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Classifier based graph construction for video segmentation
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Graph-based methods, enabling topperformance on recent benchmarks, consist of three essential components: 1. powerful features account for object appearance and motion similarities; 2. spatio-temporal ...
While a wide variety of features have been explored and various graph partition algorithms have been proposed, there is surprisingly little research on how to construct a graph to obtain the best video ...
To the best of our knowledge, graph construction based on classifier-learnt combination of features is novel in video segmentation. ...
doi:10.1109/cvpr.2015.7298697
dblp:conf/cvpr/KhorevaG0S15
fatcat:i3mekzwiubasrmobrejh4l7oba
ACGNet: Action Complement Graph Network for Weakly-Supervised Temporal Action Localization
2022
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In this paper, we tackle this problem from a new perspective by enhancing segment-level representations with a simple yet effective graph convolutional network, namely action complement graph network ( ...
It facilitates the current video segment to perceive spatial-temporal dependencies from others that potentially convey complementary clues, implicitly mitigating the negative effects caused by the two ...
The action complement graph (ACG) is constructed for each video in a principled way to exchange complementary information between its nodes (i.e., segments). ...
doi:10.1609/aaai.v36i3.20216
fatcat:brxtspeelrcbjnyselb3dj5oei
A Study on Relational Semantic Video Content Extraction Using Bayesian Network Classifier
2014
IOSR Journal of Computer Engineering
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The VISCOM is used to construct ontology for a given domain and the rule based model is used to define some complex situation more effectively. ...
The content based extraction in videos is an important application due to the rapid growth in the video based application. ...
If the resulted segmented video is over segmented the subsequent region merging operation is done and the region adjacency graph is constructed. ...
doi:10.9790/0661-1631106109
fatcat:5zt3umncmba4tft3dob7siqjga
Ensemble Video Object Cut in Highly Dynamic Scenes
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition
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We incorporate this similarity information into a graph-cut energy minimization framework for foreground object segmentation. ...
We consider video object cut as an ensemble of framelevel background-foreground object classifiers which fuses information across frames and refine their segmentation results in a collaborative and iterative ...
Foreground Salience Graph. Based on the temporal and spatial salience measures, we can then construct the foreground salience graph. ...
doi:10.1109/cvpr.2013.254
dblp:conf/cvpr/RenHH13
fatcat:va5zydzufbe33ixhfajpekrkzu
Indian classical dance action identification using adaptive graph matching from unconstrained videos
2017
International Journal of Engineering & Technology
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A new segmentation model is developed using discrete wavelet transform and local binary pattern features for segmentation. ...
The extracted features were represented as a graph and a novel adaptive graph matching algorithm is proposed. ...
For recognition, an adaptive graph matching algorithm is proposed to classify query dance video based on the dance dataset. ...
doi:10.14419/ijet.v7i1.1.10156
fatcat:jsuzbgkwhbfevcm4ke57rrzu6y
ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization
[article]
2021
arXiv
pre-print
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In this paper, we tackle this problem from a new perspective by enhancing segment-level representations with a simple yet effective graph convolutional network, namely action complement graph network ( ...
It facilitates the current video segment to perceive spatial-temporal dependencies from others that potentially convey complementary clues, implicitly mitigating the negative effects caused by the two ...
The action complement graph interference provide relatively stable information and low-
(ACG) is constructed for each video in a principled way quality segments can also be complementary ...
arXiv:2112.10977v1
fatcat:r5xj6pnstzelzbhysluzscf5vq
Segmentation Improved Label Propagation for Semi-supervised Anomaly Detection in Fused Magnesia Furnace Process
2020
IEEE Access
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The second part is to train a LSTM classifier for the detection of semi-molten working condition. The classifier is trained under the framework of graph based label propagation. ...
The graph based label propagation is used to iterative spread the labeling information and train a LSTM classifier. ...
doi:10.1109/access.2020.3042464
fatcat:54x5owbicba2netihg7kx4ns3q
Action recognition in video using a spatial-temporal graph-based feature representation
2015
2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
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We propose a video graph based human action recognition framework. ...
In particular, we extend a popular dbscan density-based clustering algorithm to form an intuitive video graph. ...
Graph-cut based methods have achieved impressive performance for object segmentation, even on difficult image datasets [6] . ...
doi:10.1109/avss.2015.7301760
dblp:conf/avss/JargalsaikhanLT15
fatcat:o3ni4voco5eq5lucohurfmorra
A Generic Framework for Video Annotation via Semi-Supervised Learning
2012
IEEE transactions on multimedia
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Concretely, a Fast Graph-based Semi-Supervised Multiple Instance Learning (FGSSMIL) algorithm, which aims to simultaneously tackle these difficulties in a generic framework for various video domains (e.g ...
Two critical issues of FGSSMIL are: 1) how to calculate the weight assignment for a graph construction, where the weight of an edge specifies the similarity between two data points. ...
Similarity Measure for Graph Construction More recently, graph-based methods have attracted the interest of researchers in this community due to their effectiveness and computational efficiency (most graph-based ...
doi:10.1109/tmm.2012.2191944
fatcat:7uaujwzq4nfrto5jaim7bf4ify
Self-propagating video segmentation using patch matching and enhanced Onecut
2020
SN Applied Sciences
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constructed to obtain its segmentation result. ...
In this paper, a self-propagating video segmentation approach based on patch matching and enhanced Onecut is proposed, which takes full advantage of the target's color feature, shape feature, and motion ...
Enhanced Onecut segmentation model In recent years, video segmentation method based on graph cuts framework [24] [25] [26] [27] has become a research hotspot. ...
doi:10.1007/s42452-020-2033-8
fatcat:pi6z32f2gndhjab2gbvprueyzq
Graph-based foreground extraction in extended color space
2009
2009 16th IEEE International Conference on Image Processing (ICIP)
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For efficient computation, the graph structure is optimized by the minimum spanning tree before segmentation. ...
Then the foreground region is extracted with a graph-based region segmentation method considering background difference and spatial homogeneity. ...
Mu et al. have proposed a combination method of block-based initial segmentation and refinement by Graph-cut for automatic segmentation [7] . ...
doi:10.1109/icip.2009.5414370
dblp:conf/icip/KimH09
fatcat:iwb6cy2q5zemjorwhpgq25iqqa
FaceSeg: Automatic Face Segmentation for Real-Time Video
2009
IEEE transactions on multimedia
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In this paper, we present an accurate segmentation system for cutting human faces out from video sequences in real-time. ...
Segmenting human faces automatically is very important for face recognition and verification, security system, and computer vision. ...
A statistical model-based video segmentation for head-andshoulder type video is addressed in [16] . ...
doi:10.1109/tmm.2008.2008922
fatcat:ozoih5kj2rfr7lzbdy4huqwtkm
Fuzzy Ontology and Rule based Model for Automatic Semantic Content Extraction from Videos using k-Means Algorithm
2015
International Journal of Computer Applications
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Video based applications disclosed need for efficiently extracting and modeling the video contents. The video features can be classified into normal data, relative features and logic content. ...
Semantic level understanding is required for core content of video. So to get video content automatic semantic content framework is proposed. ...
Fig3 shows resuts for frame segmention of a given video. i.e. number of segments done in a frame. In proposed system frame is divided into less number of segments than genetic based approach. ...
doi:10.5120/ijca2015907148
fatcat:uckz54b5mbgapfzj3bn6emcbbq
A generic framework for event detection in various video domains
2010
Proceedings of the international conference on Multimedia - MM '10
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Concretely, a Graph-based Semi-Supervised Multiple Instance Learning (GSSMIL) algorithm is proposed to jointly explore small-scale expert labeled videos and large-scale unlabeled videos to train the event ...
A critical issue of GSSMIL in constructing a graph is the weight assignment, where the weight of an edge specifies the similarity between two data points. ...
To effectively obtain the similarity measure for the graph construction, we present an MILIS measure by considering the class structure. ...
doi:10.1145/1873951.1873967
dblp:conf/mm/ZhangXZLL10
fatcat:aznsm4tedzak3nn4ifq22x2p3y
Improved Image Boundaries for Better Video Segmentation
[chapter]
2016
Lecture Notes in Computer Science
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Graph-based video segmentation methods rely on superpixels as starting point. ...
While most previous work has focused on the construction of the graph edges and weights as well as solving the graph partitioning problem, this paper focuses on better superpixels for video segmentation ...
As our approach is directly applicable to any graph-based video segmentation technique we additionaly evaluated our superpixels with the classifier-based graph construction method of [24] . ...
doi:10.1007/978-3-319-49409-8_64
fatcat:ow7hvcxcvva7lj5v24ioa25rli
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