A unified scheme of shot boundary detection and anchor shot detection in news video story parsing

Lee, Hansung; Yu, Jaehak; Im, Youngjae; Gil, Jorge; Park, Jooyoung

doi:10.1007/s11042-010-0462-x

Cited by 46 publications

(13 citation statements)

References 16 publications

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“…Lee et al propose a unified scheme of shot boundary and anchor shot detection in news videos [33]. Their method relies on a singular value decomposition and the socalled Kernel-ART method.…”

Section: Related Workmentioning

confidence: 99%

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Tensor-Based Shot Boundary Detection in Video Streams

Cyganek

Woźniak

2017

New Gener. Comput.

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This paper presents a method for content change detection in multidimensional video signals. Video frames are represented as tensors of order consistent with signal dimensions. The method operates on unprocessed signals and no special feature extraction is assumed. The dynamic tensor analysis method is used to build a tensor model from the stream. Each new datum in the stream is then compared to the model with the proposed concept drift detector. If it fits, then a model is updated. Otherwise, a model is rebuilt, starting from that datum, and the signal shot is recorded. The proposed fast tensor decomposition algorithm allows efficient operation compared to the standard tensor decomposition method. Experimental results show many useful properties of the method, as well as its potential further extensions and applications.

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Section: Related Workmentioning

confidence: 99%

“…These methods, in turn, rely on efficient algorithms of boundary detection in the visual streams. Basically, the summarization methods are divided into static and dynamic ones [33,35]. In the former, a set of consecutive frames with sufficiently coherent contents is represented by a single representative frame, called a key frame.…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based Shot Boundary Detection in Video Streams

Cyganek

Woźniak

2017

New Gener. Comput.

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“…The most traditional way to realize automatic TV program segmentation is either classification strategies [10,27] or event detection approaches [4,17], which are mostly supervised approaches. Unsupervised approaches for program segmentation were also addressed recently, where audiovisual consistency [5] and clustering-based methods [15] are considered.…”

Section: Introductionmentioning

confidence: 99%

Content-based unsupervised segmentation of recurrent TV programs using grammatical inference

Vallet

Carrive

et al. 2017

Multimed Tools Appl

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TV program segmentation raised as a major topic in the last decade for the task of high quality indexing of multimedia content. Earlier studies of TV program segmentation are either highly supervised (e.g., event detection) or too specific to a certain type of program (e.g., cluster-based methods), which is not practically usable for indexing tasks because of the lack of generality of programs types. In this paper, we address the problem of unsupervised TV program segmentation by leveraging grammatical inference, i.e., discovering a common structural model shared by a collection of episodes of a recurrent TV program by finding an optimal alignment of structural elements across episodes. Structural elements referring to a video segment with a particular syntactic meaning with respect to the video structure. The use of symbolic representation of structural elements makes grammatical inference feasible to be applied on TV program modeling, and makes TV program segmentation possible to rely on only minimal domain knowledge. The proposed approach is operated in two phases. The first phase aims at obtaining a symbolic representation of each episode, where the elements relevant to the structure are discovered based on recurrence mining. The second phase is that of grammatical inference from the symbolic representation of episodes. We investigate two inference techniques, one based on multiple sequence alignment and one relying on uniform resampling, to infer structural grammars for TV programs. A model of the structure is derived from the structural grammars and used to predict the structure of new episodes. Comparative evaluation on two gramBingqing Qu

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“…Zhe-Ming Lu et al extract color histograms in the HSV space to form a frame feature matrix and singular value decomposition (SVD) is then performed on the frame feature matrices of all candidate segments to reduce the feature dimension [4]. Hansung et al adopt singular value to decompose the color histogram feature vector and discard the small singular value and then use the clustering method to find video shot 5th International Conference on Advanced Engineering Materials and Technology (AEMT 2015) boundaries [5]. Jiang et al carry out the non-uniform block according to the different underlying features of the video frames and then the shot boundary is detected by using the feature matching of speed-up robust features (SURF) [6].…”

Section: Introductionmentioning

confidence: 99%

Shot Boundary Detection Algorithm Based on HSV Histogram and HOG Feature

Shao¹,

Qu²,

Cui³

2015

Proceedings of the 2015 International Conference on Advanced Engineering Materials and Technology

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Keywords: HSV color histogram, HOG feature, shot boundary detection. Abstract.Spatial information can not be contained in the statistical feature of hue-saturation-value(HSV) color histogram, moreover, which is not robust for the videos with the similarity of background information and affected by illumination. Therefore, a new video shot segment method based on HSV histogram and Histogram of Gradient (HOG) feature is proposed. Firstly,HSV color histogram is used to detect the difference between two adjacent frames which is then compared with the adaptive threshold. After the initial detection of the shot boundary is finished, HOG feature of the video is adopted to make further shot boundary detection，which eliminates the wrong shot boundaries and adds missed shot boundaries. Experimental results indicate that the proposed method achieves higher accuracy and recall.

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A unified scheme of shot boundary detection and anchor shot detection in news video story parsing

Cited by 46 publications

References 16 publications

Tensor-Based Shot Boundary Detection in Video Streams

Tensor-Based Shot Boundary Detection in Video Streams

Content-based unsupervised segmentation of recurrent TV programs using grammatical inference

Shot Boundary Detection Algorithm Based on HSV Histogram and HOG Feature

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