Implementing Content Based Video Retrieval Using Speeded-Up Robust Features

2019 ◽  
Author(s):  
Jos Timanta Tarigan ◽  
Poltak Sihombing ◽  
Evi Marpaung
Keyword(s):  
Video Retrieval ◽  
Speeded Up Robust Features ◽  
Content Based Video Retrieval

scholarly journals Content based video retrieval system using principal object analysis

2019 ◽  
Vol 14 (9) ◽  
pp. 24
Author(s):  
Bui Van Thinh ◽  
Tran Anh Tuan ◽  
Ngo Quoc Viet ◽  
Pham The Bao
Keyword(s):  
Retrieval System ◽  
Video Retrieval ◽  
Music History ◽  
Input Image ◽  
Speeded Up Robust Features ◽  
Video Clips ◽  
Tv Program ◽  
Retrieval Result ◽  
Program Show ◽  
Content Based Video Retrieval

Video retrieval is a searching problem on videos or clips based on the content of video clips which relates to the input image or video. Some recent approaches have been in challenging problem due to the diversity of video types, frame transitions and camera positions. Besides, that an appropriate measures is selected for the problem is a question. We propose a content based video retrieval system in some main steps resulting in a good performance. From a main video, we process extracting keyframes and principal objects using Segmentation of Aggregating Superpixels (SAS) algorithm. After that, Speeded Up Robust Features (SURF) are selected from those principal objects. Then, the model “Bag-of-words” in accompanied by SVM classification are applied to obtain the retrieval result. Our system is evaluated on over 300 videos in diversity from music, history, movie, sports, and natural scene to TV program show. 

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.

Sign in / Sign up

Export Citation Format

Share Document