375 research outputs found
Scale-Adaptive Video Understanding.
The recent rise of large-scale, diverse video data has urged a new era of high-level video understanding. It is increasingly critical for intelligent systems to extract semantics from videos. In this dissertation, we explore the use of supervoxel hierarchies as a type of video representation for high-level video understanding. The supervoxel hierarchies contain rich multiscale decompositions of video content, where various structures can be found at various levels. However, no single level of scale contains all the desired structures we need. It is essential to adaptively choose the scales for subsequent video analysis. Thus, we present a set of tools to manipulate scales in supervoxel hierarchies including both scale generation and scale selection methods.
In our scale generation work, we evaluate a set of seven supervoxel methods in the context of what we consider to be a good supervoxel for video representation. We address a key limitation that has traditionally prevented supervoxel scale generation on long videos. We do so by proposing an approximation framework for streaming hierarchical scale generation that is able to generate multiscale decompositions for arbitrarily-long videos using constant memory.
Subsequently, we present two scale selection methods that are able to adaptively choose the scales according to application needs. The first method flattens the entire supervoxel hierarchy into a single segmentation that overcomes the limitation induced by trivial selection of a single scale. We show that the selection can be driven by various post hoc feature criteria. The second scale selection method combines the supervoxel hierarchy with a conditional random field for the task of labeling actors and actions in videos. We formulate the scale selection problem and the video labeling problem in a joint framework. Experiments on a novel large-scale video dataset demonstrate the effectiveness of the explicit consideration of scale selection in video understanding.
Aside from the computational methods, we present a visual psychophysical study to quantify how well the actor and action semantics in high-level video understanding are retained in supervoxel hierarchies. The ultimate findings suggest that some semantics are well-retained in the supervoxel hierarchies and can be used for further video analysis.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133202/1/cliangxu_1.pd
Point-wise mutual information-based video segmentation with high temporal consistency
In this paper, we tackle the problem of temporally consistent boundary
detection and hierarchical segmentation in videos. While finding the best
high-level reasoning of region assignments in videos is the focus of much
recent research, temporal consistency in boundary detection has so far only
rarely been tackled. We argue that temporally consistent boundaries are a key
component to temporally consistent region assignment. The proposed method is
based on the point-wise mutual information (PMI) of spatio-temporal voxels.
Temporal consistency is established by an evaluation of PMI-based point
affinities in the spectral domain over space and time. Thus, the proposed
method is independent of any optical flow computation or previously learned
motion models. The proposed low-level video segmentation method outperforms the
learning-based state of the art in terms of standard region metrics
Unsupervised Action Proposal Ranking through Proposal Recombination
Recently, action proposal methods have played an important role in action
recognition tasks, as they reduce the search space dramatically. Most
unsupervised action proposal methods tend to generate hundreds of action
proposals which include many noisy, inconsistent, and unranked action
proposals, while supervised action proposal methods take advantage of
predefined object detectors (e.g., human detector) to refine and score the
action proposals, but they require thousands of manual annotations to train.
Given the action proposals in a video, the goal of the proposed work is to
generate a few better action proposals that are ranked properly. In our
approach, we first divide action proposal into sub-proposal and then use
Dynamic Programming based graph optimization scheme to select the optimal
combinations of sub-proposals from different proposals and assign each new
proposal a score. We propose a new unsupervised image-based actioness detector
that leverages web images and employs it as one of the node scores in our graph
formulation. Moreover, we capture motion information by estimating the number
of motion contours within each action proposal patch. The proposed method is an
unsupervised method that neither needs bounding box annotations nor video level
labels, which is desirable with the current explosion of large-scale action
datasets. Our approach is generic and does not depend on a specific action
proposal method. We evaluate our approach on several publicly available trimmed
and un-trimmed datasets and obtain better performance compared to several
proposal ranking methods. In addition, we demonstrate that properly ranked
proposals produce significantly better action detection as compared to
state-of-the-art proposal based methods
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