An efficient video shot representation for fast video retrieval

Author(s):  
Cheng Cai ◽  
Kin-Man Lam ◽  
Zheng Tan
Author(s):  
Nandini H. M. ◽  
Chethan H. K. ◽  
Rashmi B. S.

Shot boundary detection in videos is one of the most fundamental tasks towards content-based video retrieval and analysis. In this aspect, an efficient approach to detect abrupt and gradual transition in videos is presented. The proposed method detects the shot boundaries in videos by extracting block-based mean probability binary weight (MPBW) histogram from the normalized Kirsch magnitude frames as an amalgamation of local and global features. Abrupt transitions in videos are detected by utilizing the distance measure between consecutive MPBW histograms and employing an adaptive threshold. In the subsequent step, co-efficient of mean deviation and variance statistical measure is applied on MPBW histograms to detect gradual transitions in the video. Experiments were conducted on TRECVID 2001 and 2007 datasets to analyse and validate the proposed method. Experimental result shows significant improvement of the proposed SBD approach over some of the state-of-the-art algorithms in terms of recall, precision, and F1-score.


2011 ◽  
Vol 10 (03) ◽  
pp. 247-259 ◽  
Author(s):  
Dianting Liu ◽  
Mei-Ling Shyu ◽  
Chao Chen ◽  
Shu-Ching Chen

In consequence of the popularity of family video recorders and the surge of Web 2.0, increasing amounts of videos have made the management and integration of the information in videos an urgent and important issue in video retrieval. Key frames, as a high-quality summary of videos, play an important role in the areas of video browsing, searching, categorisation, and indexing. An effective set of key frames should include major objects and events of the video sequence, and should contain minimum content redundancies. In this paper, an innovative key frame extraction method is proposed to select representative key frames for a video. By analysing the differences between frames and utilising the clustering technique, a set of key frame candidates (KFCs) is first selected at the shot level, and then the information within a video shot and between video shots is used to filter the candidate set to generate the final set of key frames. Experimental results on the TRECVID 2007 video dataset have demonstrated the effectiveness of our proposed key frame extraction method in terms of the percentage of the extracted key frames and the retrieval precision.


Author(s):  
LIANG-HUA CHEN ◽  
KUO-HAO CHIN ◽  
HONG-YUAN MARK LIAO

The usefulness of a video database depends on whether the video of interest can be easily located. In this paper, we propose a video retrieval algorithm based on the integration of several visual cues. In contrast to key-frame based representation of shot, our approach analyzes all frames within a shot to construct a compact representation of video shot. In the video matching step, by integrating the color and motion features, a similarity measure is defined to locate the occurrence of similar video clips in the database. Therefore, our approach is able to fully exploit the spatio-temporal information contained in video. Experimental results indicate that the proposed approach is effective and outperforms some existing technique.


2020 ◽  
Vol 8 (5) ◽  
pp. 4763-4769

Now days as the progress of digital image technology, video files raise fast, there is a great demand for automatic video semantic study in many scenes, such as video semantic understanding, content-based analysis, video retrieval. Shot boundary detection is an elementary step for video analysis. However, recent methods are time consuming and perform badly in the gradual transition detection. In this paper we have projected a novel approach for video shot boundary detection using CNN which is based on feature extraction. We designed couple of steps to implement this method for automatic video shot boundary detection (VSBD). Primarily features are extracted using H, V&S parameters based on mean log difference along with implementation of histogram distribution function. This feature is given as an input to CNN algorithm which detects shots which is based on probability function. CNN is implemented using convolution and rectifier linear unit activation matrix which is followed after filter application and zero padding. After downsizing the matrix it is given as a input to fully connected layer which indicates shot boundaries comparing the proposed method with CNN method based on GPU the results are encouraging with substantially high values of precision Recall & F1 measures. CNN methods perform moderately better for animated videos while it excels for complex video which is observed in the results.


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