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Key-frame-extraction has been recognized the important research issue in the content based on video retrieval. And the effectiveness of the key frames will directly influence on video retrieval. This paper proposes a new method of video key-frame-extraction based on block local features and mean shift clustering. Firstly, we partition the image by the block-weighted strategy, and then extract the...
In this paper, we propose a new method based on wavelet transform, statistical features and central moments for both graphics and scene text detection in video images. The method uses wavelet single level decomposition LH, HL and HH subbands for computing features and the computed features are fed to k means clustering to classify the text pixel from the background of the image. The average of wavelet...
This paper describes a system for automatically extracting meta-information on people from videos on the Web. The system contains multiple modules which automatically track people, including both faces and bodies, and clusters the people into distinct groups. We present new technology and significantly modify existing algorithms for body-detection, shot-detection and grouping, tracking, and track-clustering...
Video key-frame extraction using unsupervised clustering is an effective method to get key-frame from video clips. When multi-features are used to cluster frames, different features usually have different weight and importance. This paper introduces a feature weight based clustering method which detects the optimize cluster number and performs clustering at the same time. Starting with an over-specified...
This paper focuses on a specific type of unedited video content, called rushes, which are used for movie editing and usually present a high-level of redundancy. Our goal is to automatically extract a summarized preview, where redundant material is diminished without discarding any important event. To achieve this, rushes content has been first analysed and modeled. Then different clustering techniques...
In this paper, we propose a novel hierarchical framework for soccer (football) video classification. Unlike most existing video classification approaches, which focus on shot detection followed by classification based on clustering using shot aggregation, the proposed scheme perform a top-down video scene classification which avoids shot clustering. This improves the classification accuracy and also...
This paper presents a method which able to integrate audio and visual information for action scene analysis in any movie. The approach is top-down for determining and extract action scenes in video by analyzing both audio and video data. In this paper, we directly modelled the hierarchy and shared structures of human behaviours, and we present a framework of the hidden Markov model based application...
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