The Experts below are selected from a list of 30609 Experts worldwide ranked by ideXlab platform
Shuching Chen - One of the best experts on this subject based on the ideXlab platform.
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Scene Change detection by audio and video clues
International Conference on Multimedia and Expo, 2002Co-Authors: Shuching Chen, Meiling Shyu, Wenhui Liao, Chengcui ZhangAbstract:Automatic video Scene Change detection is a challenging task. Using audio or visual information alone often cannot provide a satisfactory solution. However, how to combine audio and visual information efficiently still remains a difficult issue since there are various cases in their relationship due to the versatility of videos. We present an effective Scene Change detection method that adopts the joint evaluation of the audio and visual features. First, video information is used to find the shot boundaries. Second, the audio features for each video shot can be extracted. Lastly, an audio-video combination schema is proposed to detect the video Scene boundaries.
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video Scene Change detection method using unsupervised segmentation and object tracking
International Conference on Multimedia and Expo, 2001Co-Authors: Shuching Chen, Meiling Shyu, Chengcui Zhang, R L KashyapAbstract:In order to manage the growing amount of video information efficiently, a video Scene Change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the Scene Change detection to organize the video content. In this paper, we present an effective Scene Change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate Scene Change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis.
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ICME - Video Scene Change detection method using unsupervised segmentation and object tracking
IEEE International Conference on Multimedia and Expo 2001. ICME 2001., 2001Co-Authors: Shuching Chen, Meiling Shyu, Chengcui Zhang, R L KashyapAbstract:In order to manage the growing amount of video information efficiently, a video Scene Change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the Scene Change detection to organize the video content. In this paper, we present an effective Scene Change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate Scene Change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis.
Chengcui Zhang - One of the best experts on this subject based on the ideXlab platform.
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Scene Change detection by audio and video clues
International Conference on Multimedia and Expo, 2002Co-Authors: Shuching Chen, Meiling Shyu, Wenhui Liao, Chengcui ZhangAbstract:Automatic video Scene Change detection is a challenging task. Using audio or visual information alone often cannot provide a satisfactory solution. However, how to combine audio and visual information efficiently still remains a difficult issue since there are various cases in their relationship due to the versatility of videos. We present an effective Scene Change detection method that adopts the joint evaluation of the audio and visual features. First, video information is used to find the shot boundaries. Second, the audio features for each video shot can be extracted. Lastly, an audio-video combination schema is proposed to detect the video Scene boundaries.
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video Scene Change detection method using unsupervised segmentation and object tracking
International Conference on Multimedia and Expo, 2001Co-Authors: Shuching Chen, Meiling Shyu, Chengcui Zhang, R L KashyapAbstract:In order to manage the growing amount of video information efficiently, a video Scene Change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the Scene Change detection to organize the video content. In this paper, we present an effective Scene Change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate Scene Change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis.
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ICME - Video Scene Change detection method using unsupervised segmentation and object tracking
IEEE International Conference on Multimedia and Expo 2001. ICME 2001., 2001Co-Authors: Shuching Chen, Meiling Shyu, Chengcui Zhang, R L KashyapAbstract:In order to manage the growing amount of video information efficiently, a video Scene Change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the Scene Change detection to organize the video content. In this paper, we present an effective Scene Change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate Scene Change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis.
Liangpei Zhang - One of the best experts on this subject based on the ideXlab platform.
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unsupervised Scene Change detection via latent dirichlet allocation and multivariate alteration detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Yong Wang, Liangpei ZhangAbstract:Scene Change detection is the process of identifying the differences between the multitemporal image Scenes, which has significant potential in the application of urban development and land management at the semantic level. Traditional Scene Change detection methods are based on the supervised Scene classification, and then directly compare the independent classification results without considering the temporal correlation between the unChanged regions. However, few studies have focused on detecting the semantic Changes of multitemporal image Scenes with unsupervised methods. In this paper, we propose a novel unsupervised Scene Change detection method by using latent Dirichlet allocation (LDA) and multivariate alteration detection (MAD). First, the Scene is represented by the bag-of-visual-words model, and adopt the union dictionary to ensure the consistency of dictionary space. Then, LDA is used to achieve the middle-level feature dimension reduction, and generate the common topic space of the two multitemporal image Scene datasets. And finally, the MAD method was applied to detect the semantic Changes of corresponding multitemporal image Scenes. Two experiments with high-resolution remote sensing image Scene datasets demonstrated that our proposed approach can get a better performance in unsupervised Scene Change detection without prior knowledge.
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Kernel Slow Feature Analysis for Scene Change Detection
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Liangpei ZhangAbstract:Scene Change detection between multitemporal image Scenes can be used to interpret the variation of regional land use, and has significant potential in the application of urban development monitoring at the semantic level. The traditional methods directly comparing the independent semantic classes neglect the temporal correlation, and thus suffer from accumulated classification errors. In this paper, we propose a novel Scene Change detection method via kernel slow feature analysis (KSFA) and postclassification fusion, which integrates independent Scene classification with Scene Change detection to accurately determine Scene Changes and identify the “from-to” transition type. After representation with the bag-of-visual-words model, KSFA is proposed to extract the nonlinear temporally invariant features, to better measure the Change probability between corresponding multitemporal image Scenes. Two postclassification fusion methods, which are based on Bayesian theory and predefined rules, respectively, are then employed to identify the optimal coupled class combinations of multitemporal Scene pairs. Furthermore, in addition to identifying semantic Changes, the proposed method can also improve the performance of Scene classification, since the unChanged Scenes are more likely to belong to the same class. Two experiments with high-resolution remote sensing image Scene data sets confirm that the proposed method can increase the accuracy of Scene Change detection, Scene transition identification, and Scene classification.
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a Scene Change detection framework for multi temporal very high resolution remote sensing images
Signal Processing, 2016Co-Authors: Lefei Zhang, Liangpei ZhangAbstract:The technology of computer vision and image processing is attracting more and more attentions in recent years, and has been applied in many research areas like remote sensing image analysis. Change detection with multi-temporal remote sensing images is very important for the dynamic analysis of landscape variations. The abundant spatial information offered by very high resolution (VHR) images makes it possible to identify the semantic classes of image Scenes. Compared with the traditional approaches, Scene Change detection can provide a new point of view for the semantic interpretation of land-use transitions. In this paper, for the first time, we explore a Scene Change detection framework for VHR images, with a bag-of-visual-words (BOVW) model and classification-based methods. Image Scenes are represented by a word frequency with three kinds of multi-temporal learned dictionary, i.e., the separate dictionary, the stacked dictionary, and the union dictionary. Three features (multispectral raw pixel; mean and standard deviation; and SIFT) and their combinations were tested in Scene Change detection. Post-classification and compound classification were evaluated for their performances in the "from-to" Change results. Two multi-temporal Scene datasets were used to quantitatively evaluate the proposed Scene Change detection approach. The results indicate that the proposed Scene Change detection framework can obtain a satisfactory accuracy and can effectively analyze land-use Changes, from a semantic point of view. A Scene Change detection framework for multi-temporal RS imagery is explored.Three different features and their combinations are tested.Three types of dictionary learning with temporal information are evaluated.It can analyze city development with semantic interpretation of land-use Change.
R L Kashyap - One of the best experts on this subject based on the ideXlab platform.
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video Scene Change detection method using unsupervised segmentation and object tracking
International Conference on Multimedia and Expo, 2001Co-Authors: Shuching Chen, Meiling Shyu, Chengcui Zhang, R L KashyapAbstract:In order to manage the growing amount of video information efficiently, a video Scene Change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the Scene Change detection to organize the video content. In this paper, we present an effective Scene Change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate Scene Change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis.
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ICME - Video Scene Change detection method using unsupervised segmentation and object tracking
IEEE International Conference on Multimedia and Expo 2001. ICME 2001., 2001Co-Authors: Shuching Chen, Meiling Shyu, Chengcui Zhang, R L KashyapAbstract:In order to manage the growing amount of video information efficiently, a video Scene Change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the Scene Change detection to organize the video content. In this paper, we present an effective Scene Change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate Scene Change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis.
Meiling Shyu - One of the best experts on this subject based on the ideXlab platform.
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Scene Change detection by audio and video clues
International Conference on Multimedia and Expo, 2002Co-Authors: Shuching Chen, Meiling Shyu, Wenhui Liao, Chengcui ZhangAbstract:Automatic video Scene Change detection is a challenging task. Using audio or visual information alone often cannot provide a satisfactory solution. However, how to combine audio and visual information efficiently still remains a difficult issue since there are various cases in their relationship due to the versatility of videos. We present an effective Scene Change detection method that adopts the joint evaluation of the audio and visual features. First, video information is used to find the shot boundaries. Second, the audio features for each video shot can be extracted. Lastly, an audio-video combination schema is proposed to detect the video Scene boundaries.
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video Scene Change detection method using unsupervised segmentation and object tracking
International Conference on Multimedia and Expo, 2001Co-Authors: Shuching Chen, Meiling Shyu, Chengcui Zhang, R L KashyapAbstract:In order to manage the growing amount of video information efficiently, a video Scene Change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the Scene Change detection to organize the video content. In this paper, we present an effective Scene Change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate Scene Change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis.
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ICME - Video Scene Change detection method using unsupervised segmentation and object tracking
IEEE International Conference on Multimedia and Expo 2001. ICME 2001., 2001Co-Authors: Shuching Chen, Meiling Shyu, Chengcui Zhang, R L KashyapAbstract:In order to manage the growing amount of video information efficiently, a video Scene Change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the Scene Change detection to organize the video content. In this paper, we present an effective Scene Change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate Scene Change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis.
