Content-based video retrieval by indexing object's motion trajectory

2002 ◽  
Author(s):  
Woongshik You ◽  
Kyu Won Lee ◽  
Jae-Gon Kim ◽  
Jinwoong Kim ◽  
Oh-Seok Kwon
Keyword(s):  
Video Retrieval ◽  
Motion Trajectory ◽  
Content Based Video Retrieval

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

scholarly journals Content based video retrieval using discrete cosine transform

2021 ◽  
Vol 21 (2) ◽  
pp. 839
Author(s):  
Sumaya Hamad ◽  
Ahmeed Suliman Farhan ◽  
Doaa Yaseen Khudhur
Keyword(s):  
Discrete Cosine Transform ◽  
Video Retrieval ◽  
Retrieval Process ◽  
Cosine Transform ◽  
Color Value ◽  
Database Average ◽  
Content Based Video Retrieval

A content based video retrieval (CBVR)framework is built in this paper.  One of the essential features of video retrieval process and CBVR is a color value. The discrete cosine transform (DCT) is used to extract a query video features to compare with the video features stored in our database. Average result of 0.6475 was obtained by using the DCT after implementing it to the database we created and collected, and on all categories. This technique was applied on our database of video, Check 100 database videos, 5 videos in each category.

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