The segmentation of news video into story units

Chaisorn, Lekha; Chua, Tat-Seng; Lee, Chin‐Hui

doi:10.1109/icme.2002.1035721

Cited by 41 publications

(21 citation statements)

References 9 publications

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“…approaches that exploit specific facts and characteristics of the motion signal that are only present in the examined domain, have also been proposed for tasks like sports [21], [22], [23] and news [24], [25] video analysis, and human action recognition [26], [27], [28]. Other elaborate approaches for motion-based recognition that employ complex statistical models include the extraction of temporal textures [29], Gibbs [30] and Gaussian [31] modeling, and more general spatiotemporal patterns [32], [33].…”

Section: Introductionmentioning

confidence: 99%

Statistical Motion Information Extraction and Representation for Semantic Video Analysis

Papadopoulos

Briassouli

Mezaris

et al. 2009

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-In this paper, an approach to semantic video analysis that is based on the statistical processing and representation of the motion signal is presented. Overall, the examined video is temporally segmented into shots and for every resulting shot appropriate motion features are extracted; using these, Hidden Markov Models (HMMs) are employed for performing the association of each shot with one of the semantic classes that are of interest. The novel contributions of this work lie in the areas of motion information processing and representation. Regarding the motion information processing, the kurtosis of the optical flow motion estimates is calculated for identifying which motion values originate from true motion rather than measurement noise. Additionally, unlike the majority of the approaches of the relevant literature that are mainly limited to global-or cameralevel motion representations, a new representation for providing local-level motion information to HMMs is also presented. It focuses only on the pixels where true motion is observed. For the selected pixels, energy distribution-related information, as well as a complementary set of features that highlight particular spatial attributes of the motion signal, are extracted. Experimental results, as well as comparative evaluation, from the application of the proposed approach in the domains of tennis, news and volleyball broadcast video, and human action video demonstrate the efficiency of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Statistical Motion Information Extraction and Representation for Semantic Video Analysis

Papadopoulos

Briassouli

Mezaris

et al. 2009

IEEE Trans. Circuits Syst. Video Technol.

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“…Scene boundary detection and analysis is therefore an increasing research topic. A segmentation of news into scenes shows [12]. Events in sport programs are searched in [13].…”

Section: Video Layers and Their Boundary Detectionmentioning

confidence: 99%

A Two-Layer Graphical Model for Combined Video Shot and Scene Boundary Detection

Al-Hames

Zettl

Wallhoff

et al. 2006

2006 IEEE International Conference on Multimedia and Expo

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In this work we present a novel two-layer hybrid Graphical model for combined shot and scene boundary detection in videos. In the first layer of the model, low-level features are used to detect shot boundaries. The shot layer is connected to a higher layer that detects scene or chapter boundaries from semantic features. With this structure, the model optimises the alignment for both layers at the same time and the detection results are interconnected. Experimental results on real video data show, that both layers highly benefit from this sharing of information. Compared to a baseline threshold method with the same features, the F-measure result for the shot detection has been improved by 12.6% absolute. For the scene boundary detection, the result has been improved by more than 11% absolute.

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“…The existing news retrieval systems in [2,3,4] are mainly the query-based retrieval, generic retrieval such as "what's hot events today? ", however, has not yet been addressed.…”

Section: Introductionmentioning

confidence: 99%

“…In the past decade, most approaches in news video retrieval focus on the news events detection [1,2]. To date, representative news video retrieval systems include Informedia project [3] and VideoQA [4].…”

Section: Introductionmentioning

confidence: 99%

Hot Event Detection and Summarization by Graph Modeling and Matching

Peng

Ngo

2005

Lecture Notes in Computer Science

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Abstract. This paper proposes a new approach for hot event detection and summarization of news videos. The approach is mainly based on two graph algorithms: optimal matching (OM) and normalized cut (NC). Initially, OM is employed to measure the visual similarity between all pairs of events under the one-to-one mapping constraint among video shots. Then, news events are represented as a complete weighted graph and NC is carried out to globally and optimally partition the graph into event clusters. Finally, based on the cluster size and globality of events, hot events can be automatically detected and selected as the summaries of news videos across TV stations of various channels and languages. Our proposed approach has been tested on news videos of 10 hours and has been found to be effective.

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The segmentation of news video into story units

Cited by 41 publications

References 9 publications

Statistical Motion Information Extraction and Representation for Semantic Video Analysis

Statistical Motion Information Extraction and Representation for Semantic Video Analysis

A Two-Layer Graphical Model for Combined Video Shot and Scene Boundary Detection

Hot Event Detection and Summarization by Graph Modeling and Matching

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