Automatic video shot detection and characterization for content-based video retrieval

2001 ◽  
Author(s):  
Jifeng Sun ◽  
Songye Cui ◽  
Xing Xu ◽  
Ying Luo
Keyword(s):  
Video Retrieval ◽  
Shot Detection ◽  
Video Shot ◽  
Content Based Video Retrieval

A Soft-Decision Histogram from the HSV Color Space for Video Shot Detection

2011 ◽  
pp. 237-253
Author(s):  
Shamik Sural ◽  
M. Mohan ◽  
A. K. Malumdar
Keyword(s):  
Video Retrieval ◽  
Color Space ◽  
Distance Measures ◽  
Relative Importance ◽  
Soft Decision ◽  
Web Based ◽  
Hsv Color Space ◽  
Saturation Intensity ◽  
Shot Detection ◽  
Video Shot

In this chapter, we describe a histogram with soft decision using the Hue, Saturation, Intensity Value (HSV) color space for effective detection of video shot boundaries. In the histogram, we choose the relative importance of hue and intensity depending on the saturation of each pixel. In traditional histograms, each pixel contributes to only one component of the histogram. However, we suggest a soft-decision approach in which each pixel contributes to two components of the histogram. We have done a detailed study of the various frame-to-frame distance measures using the proposed histogram and an Red, Green, Blue (RGB) histogram for video shot detection. The results show that the new histogram has a better shot-detection performance for each of the distance measures. A Web-based application has been developed for video retrieval, which is freely accessible to interested users.

scholarly journals CONTENT BASED VIDEO RETRIEVAL BASED ON HDWT AND SPARSE REPRESENTATION

2016 ◽  
Vol 35 (2) ◽  
pp. 67 ◽  
Author(s):  
Sajad Mohamadzadeh ◽  
Hassan Farsi
Keyword(s):  
Visual Information ◽  
Video Retrieval ◽  
Research Attention ◽  
Key Frame ◽  
New Methods ◽  
Wide Range ◽  
Video Shot ◽  
Shot Boundary ◽  
Motion Features ◽  
Content Based Video Retrieval

Video retrieval has recently attracted a lot of research attention due to the exponential growth of video datasets and the internet. Content based video retrieval (CBVR) systems are very useful for a wide range of applications with several type of data such as visual, audio and metadata. In this paper, we are only using the visual information from the video. Shot boundary detection, key frame extraction, and video retrieval are three important parts of CBVR systems. In this paper, we have modified and proposed new methods for the three important parts of our CBVR system. Meanwhile, the local and global color, texture, and motion features of the video are extracted as features of key frames. To evaluate the applicability of the proposed technique against various methods, the P(1) metric and the CC_WEB_VIDEO dataset are used. The experimental results show that the proposed method provides better performance and less processing time compared to the other methods.

An Efficient Method for Video Shot Transition Detection Using Probability Binary Weight Approach

2021 ◽  
Vol 11 (3) ◽  
pp. 1-20
Author(s):  
Nandini H. M. ◽  
Chethan H. K. ◽  
Rashmi B. S.
Keyword(s):  
Distance Measure ◽  
Video Retrieval ◽  
Experimental Result ◽  
Gradual Transition ◽  
Mean Deviation ◽  
Global Features ◽  
Video Shot ◽  
Subsequent Step ◽  
Block Based ◽  
Binary Weight

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.

scholarly journals A Distributed Content-Based Video Retrieval System for Large Data-sets

2021 ◽  
Author(s):  
ElMehdi SAOUDI ◽  
Said Jai Andaloussi
Keyword(s):  
Real Time ◽  
Retrieval System ◽  
Video Retrieval ◽  
Multimedia Databases ◽  
Video Data ◽  
Multimedia Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Data Set ◽  
Content Based Video Retrieval

Abstract With the rapid growth of the volume of video data and the development of multimedia technologies, it has become necessary to have the ability to accurately and quickly browse and search through information stored in large multimedia databases. For this purpose, content-based video retrieval ( CBVR ) has become an active area of research over the last decade. In this paper, We propose a content-based video retrieval system providing similar videos from a large multimedia data-set based on a query video. The approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key-frames for rapid browsing and efficient video indexing. We have implemented the proposed approach on both, single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments are performed using various benchmark action and activity recognition data-sets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to state-of-the-art methods.

scholarly journals Color Content based Video Retrieval using Block Truncation Coding with Different Color Spaces

2013 ◽  
Vol 64 (3) ◽  
pp. 35-38 ◽  
Author(s):  
Sudeep D.Thepade ◽  
Krishnasagar Subhedarpage ◽  
Ankur A. Mali ◽  
Tushar S. Vaidya
Keyword(s):  
Video Retrieval ◽  
Color Spaces ◽  
Block Truncation Coding ◽  
Different Color ◽  
Content Based Video Retrieval
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