Content based video retrieval system based on multimodal feature grouping by KFCM clustering algorithm to promote human–computer interaction

2020 ◽  
Author(s):  
T. Prathiba ◽  
R. Shantha Selva Kumari
Keyword(s):  
Human Computer Interaction ◽  
Clustering Algorithm ◽  
Retrieval System ◽  
Video Retrieval ◽  
Feature Grouping ◽  
Computer Interaction ◽  
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 Research on Action Recognition Method of Dance Video Image Based on Human-Computer Interaction

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
FenTian Peng ◽  
Hongkai Zhang
Keyword(s):  
Human Computer Interaction ◽  
Action Recognition ◽  
Clustering Algorithm ◽  
Recognition Rate ◽  
Video Image ◽  
Recognition Method ◽  
Skeleton Model ◽  
Video Images ◽  
Motion Characteristics ◽  
Computer Interaction

Human-computer interaction technology simplifies the complicated procedures, which aims at solving the problems of inadequate description and low recognition rate of dance action, studying the action recognition method of dance video image based on human-computer interaction. This method constructs the recognition process based on human-computer interaction technology, constructs the human skeleton model according to the spatial position of skeleton, motion characteristics of skeleton, and change angles of skeleton, describes the dance posture features by generating skeleton node graph, and extracts the key frames of dance video image by using the clustering algorithm to recognize the dance action. The experimental results show that the recognition rate of this method under different entropy values is not less than 88%. Under the test conditions of complex, dark, bright, and multiuser interference, this method can make the model to describe the dance posture accurately. Furthermore, the average recognition rates are 93.43%, 91.27%, 97.15%, and 89.99%, respectively. It is suitable for action recognition of most dance video images.

Strategi Pengembangan Content Based Video Retrieval Di Perpustakaan Sekolah Tinggi Multi Media MMTC Yogyakarta

2021 ◽  
pp. 36-60
Author(s):  
Sri Wahyuni
Keyword(s):  
Information Retrieval ◽  
System Design ◽  
Swot Analysis ◽  
Retrieval System ◽  
Video Retrieval ◽  
Information Retrieval System ◽  
User Needs ◽  
Video Content ◽  
Multi Media ◽  
Content Based Video Retrieval

ABSTRACT Introduction One of the efforts to provide the best service for users is by developing innovative library services. One of them is by developing a video content-based library collection. MMTC Yogyakarta Multi Media College Library has developed a video content-based information retrieval system. It is hoped that by utilizing this video content-based STKI, users will be helped and get accelerated information in finding the material needed, especially searching for material in video files. Data Collection Method. In this paper the writer uses qualitative research with a library research approach, while the data analysis uses content analysis techniques. This method the authors use to observe and analyze an information system. Results and Discussions. In developing a Content Based Video Retrieval strategy in the MMTC Yogyakarta Multi Media High School Library, it begins with identifying user needs, creating a system design, evaluating the system design, pouring the system design into a programming language, testing the system, evaluating the system and using it. Then, the authors also provide an overview of the development of the STKI by conducting a SWOT analysis. Based on the macro analysis, the opportunity and threat variables will be formulated, while the internal analysis will formulate the strength and weakness variables. The last stage is the STKI analysis, while the stages are: complete definition, problem analysis, needs analysis, logic design and needs analysis. Conclusions. In the Content Based Video Retrieval development strategy at the MMTC Yogyakarta Multi Media College Library, there are several things that need to be considered in the development of an information retrieval system, including: User needs, development budget (budget), human resources, support from leaders and facilities (software and hardware) and IT infrastructure (internet network). The development of the STKI should begin with identifying user needs and conducting a SWOT analysis to determine the strengths and weaknesses of the system, as well as the goal so that the system can be optimally empowered by users. Keywords: Library, Information Retrieval System, Video Content

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