]Video object segmentation by hypergraph cut

By Yuchi Huang, Qingshan Liu, Dimitris N. Metaxas. 2009

248 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Efficient hierarchical graph-based video segmentation (2010, background): “Interactive object segmentation has recently shown significant progress [1, 2, 9, 13, 16].”
Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions (2013, methodology): “Other methods ([10, 3, 21]) use appearance cues to segment each frame first and then use both appearance and motion constraints for a bottom-up final segmentation.”
Click Prediction for Web Image Reranking Using Multimodal Sparse Coding (2014, methodology): “Hypergraphs have been applied to solve problems with multil-abel learning [25] and video segmentation [26].”
3-D Object Retrieval and Recognition With Hypergraph Analysis (2012, methodology): “The hypergraph has been investigated in many data mining and information retrieval tasks [35], [37], [38], [41], [42] because of its capability of capturing the higher order relationship of samples.”
Image retrieval via probabilistic hypergraph ranking (2010, methodology): “In [17] a hypergraph cut algorithm [34] is adopted to solve the unsupervised video ob- Figure 1.”
Video Segmentation by Tracking Many Figure-Ground Segments (2013, background): “Many state-of-the-art approaches are based on the agglomerative clustering approach on supervoxels [16, 15, 37, 18]; spatial-temporal graph-cuts [36]; or tracking feature points or local regions [6, 34, 7].”
Object, Scene and Actions: Combining Multiple Features for Human Action Recognition (2010, unknown): “Our framework can be extended to handle more feature channels, like space-time interest points [29] and can beneﬁt from more complex video object segmentation methods like [15].”
Multi-view hypergraph learning by patch alignment framework (2013, background): “However, the features representing images are usually in a highdimensional space.”
Robust video segment proposals with painless occlusion handling (2015, methodology): “Among unsupervised multiple segmentation methods that generate an exhaustive list of video segments, agglomerative or spectral clustering on superpixels/supervoxels has been popular [10, 15, 19, 22, 24, 27, 36, 40, 43].”
Key-segments for video object segmentation (2011, methodology): “, [11, 4, 30], or use dense flow to cluster long-term motion trajectories [5].”
Hypergraph Neural Networks (2018, background): “In (Huang, Liu, and Metaxas 2009), hypergraph learning is further employed in video object segmentation.”
Ieee Transactions on Knowledge and Data Engineering Context-aware Hypergraph Construction for Robust Spectral Clustering (unknown): “This has been applied to many domains such as image matching [19], multi-label classification [20], video object segmentation [21], and image retrieval [22].”
Adaptive Hypergraph Learning and its Application in Image Classification (2012, unknown): “Hypergraph has been applied to problems of multilabel learning [22] and video segmentation [23].”