针对近重复视频的FD-means聚类清洗算法

近几年,随着视频数据规模的不断增加,近重复视频数据不断涌现,视频的数据质量问题越来越突出。通过近重复视频清洗方法,有助于提高视频集的数据质量。然而,目前针对近重复视频清洗问题的研究较少,主要集中于近重复视频检索等方面的研究。现有研究方法尽管可以有效识别近重复视频,但较难在保证数据完整性的前提下,自动清洗近重复视频数据,以便改善视频数据质量。为解决上述问题,提出一种融合VGG-16深度网络与FD-means（feature distance-means）聚类的近重复视频清洗方法。该方法借助MOG2模型和中值滤波算法对视频进行背景分割和前景降噪;利用VGG-16深度网络模型提取视频的深度空间特征;构建一种新的FD-means聚类算法模型,通过迭代产生的近重复视频簇,更新簇类中心点,并最终删除簇中中心点之外的近重复视频数据。实验结果表明,该方法能够有效解决近重复视频数据清洗问题,改善视频的数据质量。     

Multiscale video sequence matching for near-duplicate detection and retrieval

As one of key technologies in content-based near-duplicate detection and video retrieval, video sequence matching can be used to judge whether two videos exist duplicate or near-duplicate segments or not. Despite a lot of research efforts devoted in recent years, how to precisely and efficiently perform sequence matching among videos (which may be subject to complex audio-visual transformations) from a large-scale database still remains a pretty challenging task. To address this problem, this paper proposes a multiscale video sequence matching (MS-VSM) method, which can gradually detect and locate the similar segments between videos from coarse to fine scales. At the coarse scale, it makes use of the Maximum Weight Matching (MWM) algorithm to rapidly select several candidate reference videos from the database for a given query. Then for each candidate video, its most similar segment with respect to the given query is obtained at the middle scale by the Constrained Longest Ascending Matching Subsequence (CLAMS) algorithm, and then can be used to judge whether that candidate exists near-duplicate or not. If so, the precise locations of the near-duplicate segments in both query and reference videos are determined at the fine scale by using bi-directional scanning to check the matching similarity at the segments’ boundaries. As such, the MS-VSM method can achieve excellent near-duplicate detection accuracy and localization precision with a very high processing efficiency. Extensive experiments show that it outperforms several state-of-the-art methods remarkably on several benchmarks.

     

视频层次结构挖掘

视频处理的关键是视频信息的结构化,视频基本结构是由帧、镜头、场景和视频节目构成的层次结构。视频层次结构挖掘的一个简单框架是对视频进行镜头分割、抽取镜头特征和视频场景构造。论文在镜头分割的基础上提出了基于多特征的镜头聚类分析和基于镜头的场景边界检测两种视频场景构造方法,从而实现视频层次结构挖掘。实验表明,基于镜头的场景边界检测性能优于基于多特征的镜头聚类分析。     

基于时序结构图的视频流描述方法

通过对视频流的分解可以获得基于关键帧集的视频流表示,但这种表示方法不能反映出视频流中隐藏的故事发展关系,为揭示这种关系,提出了一种视频流的快速聚类算法,用于对视频流分解单元进行相关性分析,该算法通过检测视频镜头间的相似性和连续性,实现把来自同一摄像机的视频镜头归并入同一视频类,并帱此得到而且为矿山频流的快速浏览和检索提供了新的思路。     

用熵阈值方法进行视频场景突变探测

场景突变探测即识别出在场景内容上和前一帧有着重大差别的帧。这就要求有一个能恰当表示两相领帧之间视频内容差异的公式,计算该公式的值,并且设定阈值来确定发生场景突变的帧。该文阐述了一个用于场景探测的新方法。该方法用到了一个差异公式,而这个公式则建立在视频序列中连续帧的颜色直方图之上。为了识别场景突变,用一个熵阈值方法来自动获取阈值,而且用一个优化过程过滤错误的探测。     

基于张量分解的数据流异常检测

本文对基于分布式的演化数据流的连续异常检测问题进行了形式化描述,提出一种在滑动窗口中基于张量分解的异常检测算法--WSTA.该算法将各分布结点上的数据流作为全局数据流的子张量,通过分布结点与中心节点的通信,在分布结点的滑动窗口中自适应抽样生成概要数据结构矩阵.对该数据矩阵进行张量分解得到特征向量,然后采用基于距离的异常检测方法发现异常点.基于大量真实数据集的实验表明,此算法具有良好的适用性和可扩展性.     

AVCD-FRA: A novel solution to automatic video cut detection using fuzzy-rule-based approach

Video shot boundary detection (SBD) is a fundamental step in automatic video content analysis toward video indexing, summarization and retrieval. Despite the beneficial previous works in the literature, reliable detection of video shots is still a challenging issue with many unsolved problems. In this paper, we focus on the problem of hard cut detection and propose an automatic algorithm in order to accurately determine abrupt transitions from video. We suggest a fuzzy rule-based scene cut identification approach in which a set of fuzzy rules are evaluated to detect cuts. The main advantage of the proposed method is that, we incorporate spatial and temporal features to describe video frames, and model cut situations according to temporal dependency of video frames as a set of fuzzy rules. Also, while existing cut detection algorithms are mainly threshold dependent; our method identifies cut transitions using a fuzzy logic which is more flexible. The proposed algorithm is evaluated on a variety of video sequences from different genres. Experimental results, in comparison with the most standard cut detection algorithms confirm our method is more robust to object and camera movements as well as illumination changes.     

Video archaeology: understanding video manipulation history

Facing the explosive growth of near-duplicate videos, video archaeology is quite desired to investigate the history of the manipulations on these videos. With the determination of derived videos according to the manipulations, a video migration map can be constructed with the pair-wise relationships in a set of near-duplicate videos. In this paper, we propose an improved video archaeology (I-VA) system by extending our previous work (Shen et al. 2010). The extensions include more comprehensive video manipulation detectors and improved techniques for these detectors. Specially, the detectors are used for two categories of manipulations, i.e., semantic-based manipulations and non-semantic-based manipulations. Moreover, the improved detecting algorithms are more stable. The key of I-VA is the construction of a video migration map, which represents the history of how near-duplicate videos have been manipulated. There are various applications based on the proposed I-VA system, such as better understanding of the meaning and context conveyed by the manipulated videos, improving current video search engines by better presentation based on the migration map, and better indexing scheme based on the annotation propagation. The system is tested on a collection of 12,790 videos and 3,481 duplicates. The experimental results show that I-VA can discover the manipulation relation among the near-duplicate videos effectively.     

Dynamic Resource Allocation for Robust Distributed Multi-Point Video Conferencing

This paper proposes a distributed multi-point video conferencing system over packet erasure channels, where the aggregation of multiple video streams and resource allocation are performed in a distributed manner. Video stream combiners, which are located in different geographical areas and serve as portals for conferees, aggregate incoming streams supplied by local users with other streams aggregated from nearby video stream combiners. A packet-division multiple-access (PDMA)-based error protection scheme is proposed to be performed at each video stream combiner to minimize the maximal expected video distortion among aggregated streams. The proposed error protection scheme for multi-stream aggregation also supports user preference. In order to deliver video streams to end users with different preferred quality, a consensus algorithm is proposed to adaptively perform resource allocation based on user preference. Simulation results show that the proposed multi-stream aggregation and error protection scheme has significant gains over traditional multi-stream error protection schemes for a multi-point video conferencing system.      

Projection-detecting filter for video cut detection

This paper discusses a video cut detection method. Cut detection is an important technique for making videos easier to handle. First, this paper analyzes the distribution of the image differenceV to clarify the characteristics that makeV suitable for cut detection. We propose a cut detection method that uses a projection (an isolated sharp peak) detecting filter. A motion sensitiveV is used to stabilizeV projections at cuts, and cuts are detected more reliably with this filter. The method can achieve high detection rates without increasing the rate of misdetection. Experimental results confirm the effectiveness of the filter.