Automatic partitioning of full-motion video

Zhang, Hong-Jiang; Kankanhalli, Atreyi; Smoliar, Stephen W.

doi:10.1007/bf01210504

Cited by 1,108 publications

(602 citation statements)

References 11 publications

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“…Content-based analysis and retrieval has been extensively used for video segmentation [2], video retrieval [3], and image retrieval [1]. As discussed in [4], finding a specific person in videos is essential to understand and retrieve videos.…”

Section: Introductionmentioning

confidence: 99%

Finding Person X: Correlating Names with Visual Appearances

Yang

Chen

Hauptmann

2004

Lecture Notes in Computer Science

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Abstract. People as news subjects carry rich semantics in broadcast news video and therefore finding a named person in the video is a major challenge for video retrieval. This task can be achieved by exploiting the multi-modal information in videos, including transcript, video structure, and visual features. We propose a comprehensive approach for finding specific persons in broadcast news videos by exploring various clues such as names occurred in the transcript, face information, anchor scenes, and most importantly, the timing pattern between names and people. Experiments on the TRECVID 2003 dataset show that our approach achieves high performance.

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Section: Introductionmentioning

confidence: 99%

Finding Person X: Correlating Names with Visual Appearances

Yang

Chen

Hauptmann

2004

Lecture Notes in Computer Science

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“…Shot boundary determination has been widely studied for the last decade. Some of the early work can be found in Zhang et al (1993), Shahraray (1995) and Wang et al (2000). Further information about shot boundary determination can be found in Liu et al (2007).…”

Section: Introductionmentioning

confidence: 99%

SVM-based Relevance Feedback for semantic video retrieval

Yazdi

Javidi

Pourreza

2009

IJSISE

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This paper presents a novel method for efficient key frame extraction from video shot representation and employs a Support-Vector-Machine-based Relevance Feedback (SVM-RF) to bridging semantic gap between low-level feature and high-level concepts of shots. We introduce a new approach for key frame extraction using a hierarchical approach based on clustering. Using this key frame representation, the most representative key frame is then selected for each shot. Furthermore, our system incorporates user to judge about the result of retrieval and labelled retrieved shot in two groups, relevant and irrelevant. Then, by mean feature of relevant and irrelevant shots train an SVM classifier. In the next step, video database is classified in two groups, relevant and irrelevant shots. Suitable Graphic User Interface (GUI) is shown for capturing RF of user. This process continued until user satisfied with results. The proposed system is checked over collected shots from Trecvid2001 database and home videos include 800 shots of different concepts (10 semantic groups). Experimental results demonstrate the effectiveness of the proposed method.

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“…In sports video, almost all the shots are cut except replay transition, so for non-replay shots, we first use a two-threshold colour histogram method as in [19] to detect shot boundaries, and then classify them into the defined classes.…”

Section: View Representation and Classificationmentioning

confidence: 99%

Shot Classification in Broadcast Soccer Video

Tong

Liu²,

Lu³

2008

ELCVIA

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In this paper, we present an effective hierarchical shot classification scheme for broadcast soccer video. We first partition a video into replay and non-replay shots with replay logo detection. Then, non-replay shots are further classified into Long, Medium, Close-up or Out-field types with color and texture features based on a decision tree. We tested the method on real broadcast FIFA soccer videos, and the experimental results demonstrate its effectiveness.. IntroductionIn recent years, sports video analysis has received increasing interests due to its tremendous commercial. A popular scheme of sports video analysis has three steps: 1) parsing the video into shots; 2) classifying shots into several types with certain semantics; 3) inferring high-level events with shot transition context. In this paper, we focus on shot classification of soccer video.A lot of work has been done on shot classification in soccer video. The grass-ratio and non-field area distribution are often used as important features in most previous work [1][2][3] [12][13] to discriminate the shots of Long, Medium, and Close-up view. In [4], they used the moments of colour and shape as features to identify shot types, and modelled the shot temporal transition pattern with a Hidden Markov Model (HMM) to detect semantic events. Duan et al [5] extracted shape and motion information to categorize shots with Support Vector Machine (SVM) classifiers. Replay as a special scene of sports video, also plays an importance role in semantic event analysis. H. Pan et al [9] utilized an HMM to infer replay scenes, in which a zero-crossing measure was considered for the frequency and the amplitude of the fluctuations of adjacent frame differences. However, a single frame inside a replay must be pinpointed in advance. Later, they proposed another replay detection method based on replay-logo [14]. Kobla et al [15,16] used the macroblock, motion and bit-rate information in compressed domain to detect replay scenes. Duan et al [17] developed a logo transition detection, in which the mean shift algorithm was used to seek the mood of logo transition, and then a replay scene was determined by a pair of logo-transitions. Wang et al [18] utilized Correspondence to: qsliu@nlpr.ia.ac.cn Recommended for acceptance by Xuelong Li

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Automatic partitioning of full-motion video

Cited by 1,108 publications

References 11 publications

Finding Person X: Correlating Names with Visual Appearances

Finding Person X: Correlating Names with Visual Appearances

SVM-based Relevance Feedback for semantic video retrieval

Shot Classification in Broadcast Soccer Video

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