scholarly journals Video Summarization for Sign Languages Using the Median of Entropy of Mean Frames Method

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 748 ◽  
Author(s):  
Shazia Saqib ◽  
Syed Kazmi
Keyword(s):  
Temporal Order ◽  
Video Summarization ◽  
Sliding Window ◽  
Data Retrieval ◽  
Video Data ◽  
Original Video ◽  
Video Content ◽  
Sign Languages ◽  
The Mean ◽  
Audio Video

Multimedia information requires large repositories of audio-video data. Retrieval and delivery of video content is a very time-consuming process and is a great challenge for researchers. An efficient approach for faster browsing of large video collections and more efficient content indexing and access is video summarization. Compression of data through extraction of keyframes is a solution to these challenges. A keyframe is a representative frame of the salient features of the video. The output frames must represent the original video in temporal order. The proposed research presents a method of keyframe extraction using the mean of consecutive k frames of video data. A sliding window of size k / 2 is employed to select the frame that matches the median entropy value of the sliding window. This is called the Median of Entropy of Mean Frames (MME) method. MME is mean-based keyframes selection using the median of the entropy of the sliding window. The method was tested for more than 500 videos of sign language gestures and showed satisfactory results.

scholarly journals Multimodal Summarization of User-Generated Videos

2021 ◽  
Vol 11 (11) ◽  
pp. 5260
Author(s):  
Theodoros Psallidas ◽  
Panagiotis Koromilas ◽  
Theodoros Giannakopoulos ◽  
Evaggelos Spyrou
Keyword(s):  
Exponential Growth ◽  
Temporal Order ◽  
Video Summarization ◽  
User Generated Content ◽  
Visual Features ◽  
Original Video ◽  
Binary Classifier ◽  
Video Summaries ◽  
Audio Video ◽  
Efficient Video

The exponential growth of user-generated content has increased the need for efficient video summarization schemes. However, most approaches underestimate the power of aural features, while they are designed to work mainly on commercial/professional videos. In this work, we present an approach that uses both aural and visual features in order to create video summaries from user-generated videos. Our approach produces dynamic video summaries, that is, comprising the most “important” parts of the original video, which are arranged so as to preserve their temporal order. We use supervised knowledge from both the aforementioned modalities and train a binary classifier, which learns to recognize the important parts of videos. Moreover, we present a novel user-generated dataset which contains videos from several categories. Every 1 sec part of each video from our dataset has been annotated by more than three annotators as being important or not. We evaluate our approach using several classification strategies based on audio, video and fused features. Our experimental results illustrate the potential of our approach.

An Improved Algorithm of Keyframe Extraction for Video Summarization

2011 ◽  
Vol 225-226 ◽  
pp. 807-811
Author(s):  
Zhong Qu ◽  
Teng Fei Gao
Keyword(s):  
Video Segmentation ◽  
Video Retrieval ◽  
Video Summarization ◽  
Color Space ◽  
Original Video ◽  
Video Content ◽  
Hsv Color Space ◽  
Key Frame ◽  
Keyframe Extraction ◽  
Histogram Intersection

Video segmentation and keyframe extraction are the basis of Content-based Video Retrieval (CBVR), in which keyframe selection plays the central role in CBVR. In this paper, as the initialization of keyframe extraction, we proposed an improved approach of key-frame extraction for video summarization. In our approach, videos were firstly segmented into shots according to video content, by our improved histogram-based method, with the use of histogram intersection and nonuniform partitioning and weighting. Then, within each shot, keyframes were determined with the calculation of image entropy as a reflection of the quantity of image information in HSV color space of every frame. Our simulation results in section 4 prove that extracted key frames with our method are compact and faithful to the original video.

scholarly journals Video content analysis using the video time density function and statistical models

2021 ◽  
Author(s):  
Junfeng Jiang
Keyword(s):  
Content Analysis ◽  
Density Function ◽  
Statistical Models ◽  
Video Summarization ◽  
Video Data ◽  
Video Content ◽  
Video Content Analysis ◽  
Video Summary ◽  
Video Event Detection ◽  
Time Density

As an interesting, meaningful, and challenging topic, video content analysis is to find meaningful structure and patterns from visual data for the purpose of efficient indexing and mining of videos. In this thesis, a new theoretical framework on video content analysis using the video time density function (VTDF) and statistical models is proposed. The proposed framework tries to tackle the problems in video content analysis based on its semantic information from three perspectives: video summarization, video similarity measure, and video event detection. In particular, the main research problems are formulated mathematically first. Two kinds of video data modeling tools are then presented to explore the spatiotemporal characteristics of video data, the independent component analysis (ICA)-based feature extraction and the VTDF. Video summarization is categorized into two types: static and dynamic. Two new methods are proposed to generate the static video summary. One is hierarchical key frame tree to summarize video content hierarchically. Another is vector quantization-based method using Gaussian mixture (GM) and ICA mixture (ICAM) to explore the characteristics of video data in the spatial domain to generate a compact video summary. The VTDF is then applied to develop several approaches for content-based video analysis. In particular, VTDF-based temporal quantization and statistical models are proposed to summarize video content dynamically. VTDF-based video similarity measure model is to measure the similarity between two video sequences. VTDF-based video event detection method is to classify a video into pre-defined events. Video players with content-based fast-forward playback support are designed, developed, and implemented to demonstrate the feasibility of the proposed methods. Given the richness of literature in effective and efficient information coding and representation using probability density function (PDF), the VTDF is expected to serve as a foundation of video content representation and more video content analysis methods will be developed based on the VTDF framework.

scholarly journals Video content analysis using the video time density function and statistical models

2021 ◽  
Author(s):  
Junfeng Jiang
Keyword(s):  
Content Analysis ◽  
Density Function ◽  
Statistical Models ◽  
Video Summarization ◽  
Video Data ◽  
Video Content ◽  
Video Content Analysis ◽  
Video Summary ◽  
Video Event Detection ◽  
Time Density

As an interesting, meaningful, and challenging topic, video content analysis is to find meaningful structure and patterns from visual data for the purpose of efficient indexing and mining of videos. In this thesis, a new theoretical framework on video content analysis using the video time density function (VTDF) and statistical models is proposed. The proposed framework tries to tackle the problems in video content analysis based on its semantic information from three perspectives: video summarization, video similarity measure, and video event detection. In particular, the main research problems are formulated mathematically first. Two kinds of video data modeling tools are then presented to explore the spatiotemporal characteristics of video data, the independent component analysis (ICA)-based feature extraction and the VTDF. Video summarization is categorized into two types: static and dynamic. Two new methods are proposed to generate the static video summary. One is hierarchical key frame tree to summarize video content hierarchically. Another is vector quantization-based method using Gaussian mixture (GM) and ICA mixture (ICAM) to explore the characteristics of video data in the spatial domain to generate a compact video summary. The VTDF is then applied to develop several approaches for content-based video analysis. In particular, VTDF-based temporal quantization and statistical models are proposed to summarize video content dynamically. VTDF-based video similarity measure model is to measure the similarity between two video sequences. VTDF-based video event detection method is to classify a video into pre-defined events. Video players with content-based fast-forward playback support are designed, developed, and implemented to demonstrate the feasibility of the proposed methods. Given the richness of literature in effective and efficient information coding and representation using probability density function (PDF), the VTDF is expected to serve as a foundation of video content representation and more video content analysis methods will be developed based on the VTDF framework.

scholarly journals Video Content Encryption as a Service and Performance Implications of Video Data Encryption on Cloud

2019 ◽  
Vol 9 (1) ◽  
pp. 4718-4725
Keyword(s):  
Content Analysis ◽  
Data Retrieval ◽  
Video Data ◽  
Cloud Services ◽  
Primary Concern ◽  
Video Content ◽  
Video Content Analysis ◽  
Defining Moments ◽  
Quartic Polynomial ◽  
Encryption Algorithms

The immense growth in the video content retrieval and video content analysis have motivated the practitioners to migrate the video contents and the analytic applications on to the cloud. The cloud computing platform provides scalability for applications and data, which enables the application owners to deal with complex algorithms, which is needed for video content analysis and retrievals. The primary concern of the video data retrieval on cloud services are the weak security for the standard data during migrating from one VM to another VM. Also, the standard encryption algorithms have failed to demonstrate higher performance during encryption of a large file. Hence, the demand of the recent research is to ensure reduced performance implications for video content encryption over cloud services. This work proposes an adaptive encryption and decryption algorithm for large video data over cloud as Encryption as A Service (EAAS). This work proposes a novel key age calculation dependent on Quartic Polynomial Randomization. The quartic part utilized in the proposed calculation can produce numerous defining moments, which makes the calculation results hard to foresee and the utilization of polynomial randomization can further build the haphazardness of the defining moments. Likewise, the higher size of the video information must be diminished without rotting the data and without trading off the security. Subsequently, this work proposes a novel key edge comparability extraction procedure utilizing versatile movement. The similitude areas in the key casings contains comparable data and, in this manner, can be scrambled all around. This diminishes the time unpredictability to a more noteworthy broaden. Associated with the comparable line of advancement, this work likewise proposes time limited encryption and unscrambling calculations, which can separate between the comparable and unique areas and decrease the time intricacy further. The proposed algorithm demonstrates nearly 40% improvements over the standard encryption algorithms.

Detecting “DeepFakes” in H.264 Video Data Using Compression Ghost Artifacts

2020 ◽  
Vol 2020 (4) ◽  
pp. 116-1-116-7
Author(s):  
Raphael Antonius Frick ◽  
Sascha Zmudzinski ◽  
Martin Steinebach
Keyword(s):  
Image Forensics ◽  
Video Data ◽  
Experimental Results ◽  
Video Sequences ◽  
The Internet ◽  
Video Content ◽  
High Quality ◽  
The Public

In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.

“When you were that little…”

Gesture ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 1-40
Author(s):  
Josefina Safar
Keyword(s):  
Video Data ◽  
Yucatec Maya ◽  
Sign Languages ◽  
Linguistic Functions ◽  
The Impact

Abstract In this article, I analyse how conventional height-specifier gestures used by speakers of Yucatec Maya become incorporated into Yucatec Maya Sign Languages (YMSLs). Combining video-data from elicitation, narrations, conversations and interviews collected from YMSL signers from four communities as well as from hearing nonsigners from another Yucatec Maya village, I compare form, meaning and distribution of height-specifiers in gesture and sign. Co-speech gestures that depict the height of upright entities – performed with a flat hand, palm facing downwards – come to serve various linguistic functions in YMSLs: a noun for human referents, a verb GROW, a spatial referential device, and an element of name signs. Special attention is paid to how height-specifier gestures fulfil a grammatical purpose as noun-classifiers for human referents in YMSLs. My study demonstrates processes of lexicalisation and grammaticalisation from gesture to sign and discusses the impact of gesture on the emergence of shared sign languages.

scholarly journals Performance Evaluation of Request and Response Time for Audio and Video Data Sets

2019 ◽  
pp. 133-136
Author(s):  
Dr. Manish L Jivtode
Keyword(s):  
Web Services ◽  
Web Service ◽  
Information Exchange ◽  
Video Data ◽  
Data Sets ◽  
Representational State Transfer ◽  
State Transfer ◽  
Extensible Markup ◽  
Communications Protocol ◽  
Audio Video

Web services are applications that allow for communication between devices over the internet and are independent of the technology. The devices are built and use standardized eXtensible Markup Language (XML) for information exchange. A client or user is able to invoke a web service by sending an XML message and then gets back and XML response message. There are a number of communication protocols for web services that use the XML format such as Web Services Flow Language (WSFL), Blocks Extensible Exchange Protocol(BEEP) etc. Simple Object Access Protocol (SOAP) and Representational State Transfer (REST) are used options for accessing web services. It is not directly comparable that SOAP is a communications protocol while REST is a set of architectural principles for data transmission. In this paper, the data size of 1KB, 2KB, 4KB, 8KB and 16KB were tested each for Audio, Video and result obtained for CRUD methods. The encryption and decryption timings in milliseconds/seconds were recorded by programming extensibility points of a WCF REST web service in the Azure cloud..

scholarly journals A Research on Multi-View Video Summarization Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 6837-6846
Keyword(s):  
Video Summarization ◽  
Research Work ◽  
Original Video ◽  
Multiple Views ◽  
Public Places ◽  
Still Images ◽  
Maximum Information ◽  
The Public ◽  
Surveillance Cameras ◽  
Accident Scenarios

Video Surveillance System uses video cameras to capture images and videos that can be compressed, stored and send to place with the limited set of monitors .Now a Days all the public places such as bank, educational institutions, Offices, Hospitals are equipped with multiple surveillance cameras having overlapping field of view for security and environment monitoring purposes. A Video Summarization is a technique to generate the summary of entire Video Content either by still images or through video skim. The summarized video length should be less than the original video length and it should covers maximum information from the original video. Video summarization studies concentrating on monocular videos cannot be applied directly to multiple-view videos due to redundancy in multiple views. Generating Summary for Surveillance videos is more challenging because, videos Captured by surveillance cameras is long, contains uninteresting events, same scene recorded in different views leading to inter-view dependencies and variation in illuminations. In this paper, we present a survey on the research work carried on video summarization techniques for videos captured through multiple views. The summarized video generated can be used for the analysis of post-accident scenarios, identifying suspicious events, theft in public which supports Crime department for the investigation purposes.

Sign in / Sign up

Export Citation Format

Share Document