Multi-Modality Video Scene Segmentation Algorithm with Shot Force Competition

2014 ◽  
Vol 513-517 ◽  
pp. 514-517
Author(s):  
Yun Zhu Xiang
Keyword(s):  
Segmentation Algorithm ◽  
Scene Segmentation ◽  
Full Account ◽  
Low Level ◽  
Video Scene ◽  
Video Shot ◽  
Media Data

In order to quickly and effectively segment the video scene, a multi-modality video scene segmentation algorithm with shot force competition is proposed in this paper. This method is take full account of temporal associated co-occurrence of multimodal media data, to calculate the similarity between video shot by merging the video low-level features, then go to the video scene segmentation based on the judgment method of shot competition. The authors experiments show that the video scene can be efficiently separated by the method proposed in the paper.

A semantic-based video scene segmentation using a deep neural network

2018 ◽  
Vol 45 (6) ◽  
pp. 833-844 ◽  
Author(s):  
Hyesung Ji ◽  
Danial Hooshyar ◽  
Kuekyeng Kim ◽  
Heuiseok Lim
Keyword(s):  
Deep Learning ◽  
Semantic Analysis ◽  
Shot Boundary Detection ◽  
Data Sets ◽  
Scene Segmentation ◽  
Image Captioning ◽  
Low Level ◽  
Video Scene ◽  
Shot Boundary ◽  
High Level

Video scene segmentation is very important research in the field of computer vision, because it helps in efficient storage, indexing and retrieval of videos. Achieving this kind of scene segmentation cannot be done by just calculating the similarity of low-level features presented in the video; high-level features should also be considered to achieve a better performance. Even though much research has been conducted on video scene segmentation, most of these studies failed to semantically segment a video into scenes. Thus, in this study, we propose a Deep-learning Semantic-based Scene-segmentation model (called DeepSSS) that considers image captioning to segment a video into scenes semantically. First, the DeepSSS performs shot boundary detection by comparing colour histograms and then employs maximum-entropy-applied keyframe extraction. Second, for semantic analysis, using image captioning that benefits from deep learning generates a semantic text description of the keyframes. Finally, by comparing and analysing the generated texts, it assembles the keyframes into a scene grouped under a semantic narrative. That said, DeepSSS considers both low- and high-level features of videos to achieve a more meaningful scene segmentation. By applying DeepSSS to data sets from MS COCO for caption generation and evaluating its semantic scene-segmentation task results with the data sets from TRECVid 2016, we demonstrate quantitatively that DeepSSS outperforms other existing scene-segmentation methods using shot boundary detection and keyframes. What’s more, the experiments were done by comparing scenes segmented by humans and scene segmented by the DeepSSS. The results verified that the DeepSSS’ segmentation resembled that of humans. This is a new kind of result that was enabled by semantic analysis, which was impossible by just using low-level features of videos.

A Multiple-Feature and Multiple-Kernel Scene Segmentation Algorithm for Humanoid Robot

2014 ◽  
Vol 44 (11) ◽  
pp. 2232-2240 ◽  
Author(s):  
Zhi Liu ◽  
Shuqiong Xu ◽  
Yun Zhang ◽  
Chun Lung Philip Chen
Keyword(s):  
Humanoid Robot ◽  
Segmentation Algorithm ◽  
Scene Segmentation ◽  
Multiple Kernel ◽  
Multiple Feature

Video Scene Segmentation Using Spatial Contours and 3-D Robust Motion Estimation

2004 ◽  
Vol 14 (4) ◽  
pp. 485-497 ◽  
Author(s):  
Th. Papadimitriou ◽  
K.I. Diamantaras ◽  
M.G. Strintzis ◽  
M. Roumeliotis
Keyword(s):  
Motion Estimation ◽  
Scene Segmentation ◽  
Video Scene
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