7,379 research outputs found
Improving Streaming Video Segmentation with Early and Mid-Level Visual Processing
Despite recent advances in video segmentation, many opportunities remain to
improve it using a variety of low and mid-level visual cues. We propose
improvements to the leading streaming graph-based hierarchical video
segmentation (streamGBH) method based on early and mid level visual processing.
The extensive experimental analysis of our approach validates the improvement
of hierarchical supervoxel representation by incorporating motion and color
with effective filtering. We also pose and illuminate some open questions
towards intermediate level video analysis as further extension to streamGBH. We
exploit the supervoxels as an initialization towards estimation of dominant
affine motion regions, followed by merging of such motion regions in order to
hierarchically segment a video in a novel motion-segmentation framework which
aims at subsequent applications such as foreground recognition.Comment: WACV accepted pape
A Study of Actor and Action Semantic Retention in Video Supervoxel Segmentation
Existing methods in the semantic computer vision community seem unable to
deal with the explosion and richness of modern, open-source and social video
content. Although sophisticated methods such as object detection or
bag-of-words models have been well studied, they typically operate on low level
features and ultimately suffer from either scalability issues or a lack of
semantic meaning. On the other hand, video supervoxel segmentation has recently
been established and applied to large scale data processing, which potentially
serves as an intermediate representation to high level video semantic
extraction. The supervoxels are rich decompositions of the video content: they
capture object shape and motion well. However, it is not yet known if the
supervoxel segmentation retains the semantics of the underlying video content.
In this paper, we conduct a systematic study of how well the actor and action
semantics are retained in video supervoxel segmentation. Our study has human
observers watching supervoxel segmentation videos and trying to discriminate
both actor (human or animal) and action (one of eight everyday actions). We
gather and analyze a large set of 640 human perceptions over 96 videos in 3
different supervoxel scales. Furthermore, we conduct machine recognition
experiments on a feature defined on supervoxel segmentation, called supervoxel
shape context, which is inspired by the higher order processes in human
perception. Our ultimate findings suggest that a significant amount of
semantics have been well retained in the video supervoxel segmentation and can
be used for further video analysis.Comment: This article is in review at the International Journal of Semantic
Computin
Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations
This paper presents a co-clustering technique that, given a collection of
images and their hierarchies, clusters nodes from these hierarchies to obtain a
coherent multiresolution representation of the image collection. We formalize
the co-clustering as a Quadratic Semi-Assignment Problem and solve it with a
linear programming relaxation approach that makes effective use of information
from hierarchies. Initially, we address the problem of generating an optimal,
coherent partition per image and, afterwards, we extend this method to a
multiresolution framework. Finally, we particularize this framework to an
iterative multiresolution video segmentation algorithm in sequences with small
variations. We evaluate the algorithm on the Video Occlusion/Object Boundary
Detection Dataset, showing that it produces state-of-the-art results in these
scenarios.Comment: International Conference on Computer Vision (ICCV) 201
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
Cooperative announcement-based caching for video-on-demand streaming
Recently, video-on-demand (VoD) streaming services like Netflix and Hulu have gained a lot of popularity. This has led to a strong increase in bandwidth capacity requirements in the network. To reduce this network load, the design of appropriate caching strategies is of utmost importance. Based on the fact that, typically, a video stream is temporally segmented into smaller chunks that can be accessed and decoded independently, cache replacement strategies have been developed that take advantage of this temporal structure in the video. In this paper, two caching strategies are proposed that additionally take advantage of the phenomenon of binge watching, where users stream multiple consecutive episodes of the same series, reported by recent user behavior studies to become the everyday behavior. Taking into account this information allows us to predict future segment requests, even before the video playout has started. Two strategies are proposed, both with a different level of coordination between the caches in the network. Using a VoD request trace based on binge watching user characteristics, the presented algorithms have been thoroughly evaluated in multiple network topologies with different characteristics, showing their general applicability. It was shown that in a realistic scenario, the proposed election-based caching strategy can outperform the state-of-the-art by 20% in terms of cache hit ratio while using 4% less network bandwidth
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