Simulated evaluation of faceted browsing based on feature selection

In this paper we explore the limitations of facet based browsing which uses sub-needs of an information need for querying and organising the search process in video retrieval. The underlying assumption of this approach is that the search effectiveness will be enhanced if such an approach is employed for interactive video retrieval using textual and visual features. We explore the performance bounds of a faceted system by carrying out a simulated user evaluation on TRECVid data sets, and also on the logs of a prior user experiment with the system. We first present a methodology to reduce the dimensionality of features by selecting the most important ones. Then, we discuss the simulated evaluation strategies employed in our evaluation and the effect on the use of both textual and visual features. Facets created by users are simulated by clustering video shots using textual and visual features. The experimental results of our study demonstrate that the faceted browser can potentially improve the search effectiveness.     

Video shot boundary detection: Seven years of TRECVid activity

Shot boundary detection (SBD) is the process of automatically detecting the boundaries between shots in video. It is a problem which has attracted much attention since video became available in digital form as it is an essential pre-processing step to almost all video analysis, indexing, summarisation, search, and other content-based operations. Automatic SBD was one of the tracks of activity within the annual TRECVid benchmarking exercise, each year from 2001 to 2007 inclusive. Over those seven years we have seen 57 different research groups from across the world work to determine the best approaches to SBD while using a common dataset and common scoring metrics. In this paper we present an overview of the TRECVid shot boundary detection task, a high-level overview of the most significant of the approaches taken, and a comparison of performances, focussing on one year (2005) as an example.     

A novel compact yet rich key frame creation method for compressed video summarization

Video summarization has great potential to enable rapid browsing and efficient video indexing in many applications. In this study, we propose a novel compact yet rich key frame creation method for compressed video summarization. First, we directly extract DC coefficients of I frame from a compressed video stream, and DC-based mutual information is computed to segment the long video into shots. Then, we select shots with static background and moving object according to the intensity and range of motion vector in the video stream. Detecting moving object outliers in each selected shot, the optimal object set is then selected by importance ranking and solving an optimum programming problem. Finally, we conduct an improved KNN matting approach on the optimal object outliers to automatically and seamlessly splice these outliers to the final key frame as video summarization. Previous video summarization methods typically select one or more frames from the original video as the video summarization. However, these existing key frame representation approaches for video summarization eliminate the time axis and lose the dynamic aspect of the video scene. The proposed video summarization preserves both compactness and considerably richer information than previous video summaries. Experimental results indicate that the proposed key frame representation not only includes abundant semantics but also is natural, which satisfies user preferences.     

Exploring video content structure for hierarchical summarization

In this paper, we propose a hierarchical video summarization strategy that explores video content structure to provide the users with a scalable, multilevel video summary. First, video-shot- segmentation and keyframe-extraction algorithms are applied to parse video sequences into physical shots and discrete keyframes. Next, an affinity (self-correlation) matrix is constructed to merge visually similar shots into clusters (supergroups). Since video shots with high similarities do not necessarily imply that they belong to the same story unit, temporal information is adopted by merging temporally adjacent shots (within a specified distance) from the supergroup into each video group. A video-scene-detection algorithm is thus proposed to merge temporally or spatially correlated video groups into scenario units. This is followed by a scene-clustering algorithm that eliminates visual redundancy among the units. A hierarchical video content structure with increasing granularity is constructed from the clustered scenes, video scenes, and video groups to keyframes. Finally, we introduce a hierarchical video summarization scheme by executing various approaches at different levels of the video content hierarchy to statically or dynamically construct the video summary. Extensive experiments based on real-world videos have been performed to validate the effectiveness of the proposed approach.Published online: 15 September 2004 Corespondence to: Xingquan ZhuThis research has been supported by the NSF under grants 9972883-EIA, 9974255-IIS, 9983248-EIA, and 0209120-IIS, a grant from the state of Indiana 21^th Century Fund, and by the U.S. Army Research Laboratory and the U.S. Army Research Office under grant DAAD19-02-1-0178.     

A distance measure for repeated takes of one scene

In applications, such as post-production and archiving of audiovisual material, users are confronted with large amounts of redundant unedited raw material, called rushes. Viewing and organizing this material are crucial but time consuming tasks. Typically, multiple but slightly different takes of the same scene can be found in the rushes video. We propose a method for detecting and clustering takes of one scene shot from the same or very similar camera positions. An important subproblem is to determine the similarity of video segments. We propose a distance measure based on the Longest Common Subsequence (LCSS) model. Two variants of the proposed approach, one with a threshold parameter and one with automatically determined threshold, are compared against the Dynamic Time Warping (DTW) distance measure on six videos from the TRECVID 2007 BBC rushes summarization data set. We also evaluate the influence of the applied temporal segmentation method at the input on the results. Applications of the proposed method to automatic skimming and interactive browsing of rushes video are described.

Video summarization and retrieval using singular value decomposition

In this paper, we propose novel video summarization and retrieval systems based on unique properties from singular value decomposition (SVD). Through mathematical analysis, we derive the SVD properties that capture both the temporal and spatial characteristics of the input video in the singular vector space. Using these SVD properties, we are able to summarize a video by outputting a motion video summary with the user-specified length. The motion video summary aims to eliminate visual redundancies while assigning equal show time to equal amounts of visual content for the original video program. On the other hand, the same SVD properties can also be used to categorize and retrieve video shots based on their temporal and spatial characteristics. As an extended application of the derived SVD properties, we propose a system that is able to retrieve video shots according to their degrees of visual changes, color distribution uniformities, and visual similarities.     

An unsupervised approach to generating generic summaries of documents

We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately.     

一种层次的电影视频摘要生成方法

合理地组织视频数据对于基于内容的视频分析和检索有着重要的意义。提出了一种基于运动注意力模型的电影视频摘要生成方法。首先给出了一种基于滑动镜头窗的聚类算法将相似的镜头组织成为镜头类;然后根据电影视频场景内容的发展模式,在定义两个镜头类的3种时序关系的基础上,提出了一种基于镜头类之间的时空约束关系的场景检测方法;最后利用运动注意力模型选择场景中的重要镜头和代表帧,由选择的代表帧集合和重要镜头的关键帧集合建立层次视频摘要(场景级和镜头级)。该方法较全面地涵盖了视频内容,又突出了视频中的重要内容,能够很好地应用于电影视频的快速浏览和检索。     

MCMR: Maximum coverage and minimum redundant text summarization model

In paper, we propose an unsupervised text summarization model which generates a summary by extracting salient sentences in given document(s). In particular, we model text summarization as an integer linear programming problem. One of the advantages of this model is that it can directly discover key sentences in the given document(s) and cover the main content of the original document(s). This model also guarantees that in the summary can not be multiple sentences that convey the same information. The proposed model is quite general and can also be used for single- and multi-document summarization. We implemented our model on multi-document summarization task. Experimental results on DUC2005 and DUC2007 datasets showed that our proposed approach outperforms the baseline systems.     

A New Approach for Multi-Document Update Summarization

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基于事务序列的视频摘要挖掘方法研究

提出了一种基于事务序列的关联挖掘方法实现对足球视频的摘要挖掘。处理过程分为视频数据预处理、视频属性提取和视频摘要挖掘等三个步骤。视频数据预处理阶段将原始视频流切分成物理镜头。视频属性提取阶段先将物理镜头分成五种类型,将视频转换成镜头标识序列,以事务为单位对标识序列进行切分形成事务序列,构造一种关系数据库来存储这些事物序列数据。在视频摘要挖掘阶段,采用传统的Apriori算法对事务数据库进行关联挖掘获得关联模式,以挖掘出的关联模式为依据形成视频摘要。实验结果表明,挖掘精彩事件的查全率和查准率较高,具有实用价值。     

A topic modeling based approach to novel document automatic summarization

Most of existing text automatic summarization algorithms are targeted for multi-documents of relatively short length, thus difficult to be applied immediately to novel documents of structure freedom and long length. In this paper, aiming at novel documents, we propose a topic modeling based approach to extractive automatic summarization, so as to achieve a good balance among compression ratio, summarization quality and machine readability. First, based on topic modeling, we extract the candidate sentences associated with topic words from a preprocessed novel document. Second, with the goals of compression ratio and topic diversity, we design an importance evaluation function to select the most important sentences from the candidate sentences and thus generate an initial novel summary. Finally, we smooth the initial summary to overcome the semantic confusion caused by ambiguous or synonymous words, so as to improve the summary readability. We evaluate experimentally our proposed approach on a real novel dataset. The experiment results show that compared to those from other candidate algorithms, each automatic summary generated by our approach has not only a higher compression ratio, but also better summarization quality.     

一种基于层次分析法的视频摘要评价模型

视频摘要是对视频内容进行浓缩的一项技术,对于快速了解视频内容至关重要。如何对视频摘要的效果进行评价,是值得研究的一个问题。论文基于层次分析法构建了视频摘要评价模型,将视频摘要质量作为最终评价目标,以内容合理性和结构合理性作为准则,以内容完整性、特殊重要性、整体流畅性等作为测度层,从而建立了视频摘要评价指标体系。最后,通过对随机生成、基于特写人脸的摘要生成以及融合视音频特征的摘要生成三种算法对所提评价方法进行了实验验证,表明该方法能够有效反映出视频摘要的质量。     

Similarity Analysis of Video Sequences Using an Artificial Neural Network

Comparison of video sequences is an important operation in many multimedia information systems. The similarity measure for comparison is typically based on some measure of correlation with the perceptual similarity (or difference) amongst the video sequences or with the similarity (or difference) in some measure of semantics associated with the video sequences. In content-based similarity analysis, the video data are expressed in terms of different features. Similarity matching is then performed by quantifying the feature relationships between the target video and query video shots, with either an individual feature or with a feature combination. In this study, two approaches are proposed for the similarity analysis of video shots. In the first approach, mosaic images are created from video shots, and the similarity analysis is done by determining the similarities amongst the mosaic images. In the second approach, key frames are extracted for each video shot and the similarity amongst video shots is determined by comparing the key frames of the video shots. The features extracted include image histograms, slopes, edges, and wavelets. Both individual features and feature combinations are used in similarity matching using an artificial neural network. The similarity rank of the query video shots is determined based on the values of the coefficients of determination and the mean absolute error. The study reported in this paper shows that the mosaic-based similarity analysis can be expected to yield a more reliable result, whereas the key frame-based similarity analysis could be potentially applied to a wider range of applications. The weighted non-linear feature combination is shown to yield better results than a single feature for video similarity analysis. The coefficient of determination is shown to be a better criterion than the mean absolute error in similarity matching analysis.     

Movie scene segmentation using background information

Scene extraction is the first step toward semantic understanding of a video. It also provides improved browsing and retrieval facilities to users of video database. This paper presents an effective approach to movie scene extraction based on the analysis of background images. Our approach exploits the fact that shots belonging to one particular scene often have similar backgrounds. Although part of the video frame is covered by foreground objects, the background scene can still be reconstructed by a mosaic technique. The proposed scene extraction algorithm consists of two main components: determination of the shot similarity measure and a shot grouping process. In our approach, several low-level visual features are integrated to compute the similarity measure between two shots. On the other hand, the rules of film-making are used to guide the shot grouping process. Experimental results show that our approach is promising and outperforms some existing techniques.     

A new sentence similarity measure and sentence based extractive technique for automatic text summarization

The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of automatically creating a compressed version of a given document that provides useful information to users, and multi-document summarization is to produce a summary delivering the majority of information content from a set of documents about an explicit or implicit main topic. In our study we focus on sentence based extractive document summarization. We propose the generic document summarization method which is based on sentence clustering. The proposed approach is a continue sentence-clustering based extractive summarization methods, proposed in Alguliev [Alguliev, R. M., Aliguliyev, R. M., Bagirov, A. M. (2005). Global optimization in the summarization of text documents. Automatic Control and Computer Sciences 39, 42–47], Aliguliyev [Aliguliyev, R. M. (2006). A novel partitioning-based clustering method and generic document summarization. In Proceedings of the 2006 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI–IAT 2006 Workshops) (WI–IATW’06), 18–22 December (pp. 626–629) Hong Kong, China], Alguliev and Alyguliev [Alguliev, R. M., Alyguliev, R. M. (2007). Summarization of text-based documents with a determination of latent topical sections and information-rich sentences. Automatic Control and Computer Sciences 41, 132–140] Aliguliyev, [Aliguliyev, R. M. (2007). Automatic document summarization by sentence extraction. Journal of Computational Technologies 12, 5–15.]. The purpose of present paper to show, that summarization result not only depends on optimized function, and also depends on a similarity measure. The experimental results on an open benchmark datasets from DUC01 and DUC02 show that our proposed approach can improve the performance compared to sate-of-the-art summarization approaches.     

Summarizing high-level scene behavior

We present several novel techniques to summarize the high-level behavior in surveillance video. Our proposed methods can employ either optical flow or trajectories as input, and incorporate spatial and temporal information together, which improve upon existing approaches for summarization. To begin, we extract common pathway regions by performing graph-based clustering on similarity matrices describing the relationships between location/orientation states. We then employ the activities along the pathway regions to extract the aggregate behavioral patterns throughout scenes. We show how our summarization methods can be applied to detect anomalies, retrieve video clips of interest, and generate adaptive-speed summary videos. We examine our approaches on multiple complex urban scenes and present experimental results.     

一种层次视频摘要生成方法

视频摘要是视频内容的一种压缩表示方式。为了能够更好地浏览视频，提出了一种根据浏览或检索的粒度不同来建立两种层次视频摘要(镜头级和场景级)的思想，并给出了一种视频摘要生成方法：首先用一种根据内容变化自动提取镜头内关键帧的方法来实现关键帧的提取；继而用一种改进的时间自适应算法通过镜头的组合来得到场景；最后在场景级用最小生成树方法提取代表帧。由于关键帧和代表帧分别代表了它们所在镜头和场景的主要内容，因此它们的序列就构成了视频总结。一些电影视频片段检验的实验结果表明，这种生成方法能够较好地提供粗细两种粒度的视频内容总结。