We adapt multilevel, force-directed graph layout techniques to visualizing dynamic graphs in which vertices and edges are added and removed in an online fashion (i.e., unpredictably). We maintain multiple levels of coarseness using a dynamic, randomized coarsening algorithm. To ensure the vertices follow smooth trajectories, we employ dynamics simulation techniques, treating the vertices as point particles. We simulate fine and coarse levels of the graph simultaneously, coupling the dynamics of adjacent levels. Projection from coarser to finer levels is adaptive, with the projection determined by an affine transformation that evolves alongside the graph layouts. The result is a dynamic graph visualizer that quickly and smoothly adapts to changes in a graph.
The key frame approach can be adapted to address the online problem by computing a new key frame each time a request arrives, and then interpolating to the new key frame. For example, @cite_11 developed a system for browsing large, partially known graphs, where navigation actions add and remove subgraphs. They use force-directed layout for key frame graphs, and interpolate between them.
@cite_11
2024389296
Key frame based video summarization has emerged as an important area of research for the multimedia community. Video key frames enable an user to access any video in a friendly and meaningful way. In this paper, we propose an automated method of video key frame extraction using dynamic Delaunay graph clustering via an iterative edge pruning strategy. A structural constraint in form of a lower limit on the deviation ratio of the graph vertices further improves the video summary. We also employ an information-theoretic pre-sampling where significant valleys in the mutual information profile of the successive frames in a video are used to capture more informative frames. Various video key frame visualization techniques for efficient video browsing and navigation purposes are incorporated. A comprehensive evaluation on 100 videos from the Open Video and YouTube databases using both objective and subjective measures demonstrate the superiority of our key frame extraction method. In many video compression systems, the frames are down-sampled before transmission. Also, in many scalable systems, the residual after down- and up-sampling is encoded and transmitted. Sometimes, a few frames are encoded at normal resolution (key frames) while the other frames are encoded at reduced resolution. Super resolution can be used to enhance the up-sampling process, using motion information to improve traditional interpolation. In this paper, we propose to use a super resolution method to up-sample the non-key frames using the key frames as reference. We build dictionaries on-the- fly using the key frames instead of the traditional off-line training images. The high-frequency data of matching blocks are added to the low-resolution blocks. Since the key frames are very similar to the non-key frames, the method is robust enough to allow successful super resolution of highly compressed (severely degraded) sequences. Results are presented for many predefined block sizes, key frames frequencies, and compression parameters. In this paper, we develop a new model for recognizing human actions. An action is modeled as a very sparse sequence of temporally local discriminative key frames - collections of partial key-poses of the actor(s), depicting key states in the action sequence. We cast the learning of key frames in a max-margin discriminative framework, where we treat key frames as latent variables. This allows us to (jointly) learn a set of most discriminative key frames while also learning the local temporal context between them. Key frames are encoded using a spatially-localizable pose let-like representation with HoG and BoW components learned from weak annotations, we rely on structured SVM formulation to align our components and mine for hard negatives to boost localization performance. This results in a model that supports spatio-temporal localization and is insensitive to dropped frames or partial observations. We show classification performance that is competitive with the state of the art on the benchmark UT-Interaction dataset and illustrate that our model outperforms prior methods in an on-line streaming setting. This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames share some similarities, motion estimation is key to effective compression of these sequences. It, however, remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the point clouds as signals on the vertices of the graphs. We then cast motion estimation as a feature-matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for removing the temporal redundancy in the predictive coding of the 3D positions and the color characteristics of the point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion between consecutive frames. Moreover, motion estimation is shown to bring a significant improvement in terms of the overall compression performance of the sequence. To the best of our knowledge, this is the first paper that exploits both the spatial correlation inside each frame (through the graph) and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way.