How to prepare a perfect video abstract for your research paper – Pubrica
                     HOW TO PREPARE A PERFECT  
VIDEO ABSTRACT FOR YOUR  
RESEARCH PAPER?
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Pubrica  
Group: www.pubrica.com
Email: [email protected]
Today's discussion
In brief  
Introduction
Techniques For Observation Video 
 Abstraction
Video Abstraction Proposed 
For  Observation
Video submission guidelines 
 Conclusion
In brief
An article's abstract briefly describes a frequently  
used document to pre-select content relevant to  
the user.
The abstract and the document are written in the  
same medium, text. In the mult imedia era, video  
abstractions might be used similarly: as brief  
extracts that convey the essence of a  longer  
movie, with no interruption in the presenting  
medium.

However, the current state of the art for indexing  
and exploring huge video archives is to employ  
textual abstracts.
A video abstract is a  series of moving pictures  
taken from a lengthier movie that is significantly  
shorter than the original yet retains the original's  
essential meaning.
In t roduct ion
 WHAT IS A VIDEO ABSTRACT?
The same copyright rules apply to video abstracts to the  
corresponding article. The Video Abstract determines to emphasize 
 the most critical aspects of your research.
Your focus should be on strategies to resentment your audience's  
interest and persuade them to read your work. As you will see in  
these recommendations, high-quality videos may be made for a   
reasonable price.
 IMPORTANCE OF VIDEO ABSTRACTS
A technique for creating a video summary, which can be a sequence of 
 fixed pictures (keyframes) or moving images, is known as video  
abstraction (video skims).
In terms of browsing and direction-finding, a  good video abstract will  
allow the user to obtain the most information about the target video  
sequence in the shortest amount of t ime or enough information in the  
shortest amount of time.
Various concepts and approaches for successful video
content  abstraction have been presented throughout the years.
Here it describes the fundamental components of each method and  
how they are addressed in individual works.
Techniques For 
Observat ion  Video 
ACbosloutrr-Baascetd iToecnhniques -  Because of its insensitivity and stability 
 against changes in direction and size, the colour is the most  
effective, expressive, and essential characteristic of each  
frame/image.
Many video abstraction approaches  exploited this feature  
according to this characteristic.
The colour histogram has been frequently utilized to produce  
abstracted video by calculating the similarity between frames to  
choose representative keyframes.
Animations -  It might become difficult to convey some working  
principles or occurrences with manual  sketching abilities over time.
You can use publicly accessible animation software tools to create 
 dynamic or stationary picture graphics in such cases.
Unambiguous and straightforward representations are possible  
using programmes like Animaker, Blender, K-3D, and Plastic   A 
nimation Paper, and these are more aesthetically attractive and a  
more cost-effective method to communicate your work in a   
captivating way.
PowerPoint Presentation -  Researchers have been using 
PowerPoint  to showcase their findings on various platforms for a  
long time. This  easy method may also be utilized to describe your 
study abstract  utilizing PowerPoint's insightful pictures.
It's also free to use and comes with all of the tools you'll need to  
arrange your video abstractions with the help of clean slides. It's  
more crucial to focus on the section than the facts of your study  
while creating a narrative PowerPoint presentation for your video  
abstract.
Event-Based Video Abstraction identifies and arranges important  
events ( a t  the highest semantic level) to preserve the original film's 
 spirit. An approach that combines motion analysis with video  
skimming to create an event-based method examines the optical  
flow to find exciting events and compare them to previous data.
If no events of interest were detected, continuous frame dropping  
was employed, and those frames that featured events of interest  
were kept in the video abstract.
Combination types- The other approach for segmenting and  
classifying video segments used a mix of object and camera  
movements. The motion metrics are calculated using two optical  
flow algorithms, each employing two different keyframe selection  
criteria.
The quality of this procedure is that it can create meaningful  
abstracts for sports videos by collecting high activity material and  
being threshold-independent,  but it is a  domain-specif ic  approach.
Video Abstract ion  
Proposed  For Observat ion
Most video abstractions for research were created as browsing  
tools to make big videos easier to view and explore. On the other  
hand, video abstraction may be used as a  preprocessing stage in  
video indexing and retrieval applications.
The first stage in video analysis is pre-sampling, which picks  
appropriate frames for subsequent processing and minimizes the  
computing load. Incorrect sample rate results in a  bad video  
abstract ( low sampling rate) or a  video abstract that is too brief  
(h igh sampling rate).
The second stage in the video analysis module is feature extraction. 
 Feature selection and extraction are crucial processes that directly 
 impact the clustering algorithm's effectiveness. Feature selection  
picks prominent characteristics, simple enough, and quickly  
computed features that characterize unstructured data. In 
contrast,  feature extraction converts this input data from a high-
dimensional  space to a  lower-dimensional one.
Colour and texture are two critical aspects of any image's visual  
substance. Typical colour feature extraction approaches include  
colour histograms, colour moments, colour correlation, scalable  
colour descriptor (SCD), and colour structure descriptor (CSD).
They have computed a colour histogram in HSV colour space to  
extract colour features (which is close to human visualization). The 
 computed histogram was then quantized to 8 bins to reduce  
dimensionality without sacrificing information (3) .
Technical Specifications when C reating a video abstract, video, and 
 audio quality is critical. Making a compromise on one of these will 
 defeat producing a  multimedia abstract.
Make sure your video is recorded in a quiet environm ent with no 
 background noises.
Record your voice with a  high-quality microphone.
Record the abstract in a well- lit room to preserve an
excellent  visual of it.
If not necessary, avoid using flash photography or videography.  
Separate the narration from the video clip if possib le, and this
will  assist you in successfully managing your video's audio quality.
Accessibility- It's critical to save your movie in a  format that  
academics all around the world can understand.
Video formats such as MP4, MOV, WMV, and MKV are ideal since  
they are widely used and compatible with most operating systems. 
 Maintain an appropriate aspect ratio to accommodate all screen  
sizes.
Video submiss ion  guidel ines
The author should include video picture files as supplementary  
electronic material (ESM). When the manuscript is approved, an  
associate editor in charge of video submissions will ask the author  
to upload video pictures to MOMO (Make Our Movies Open)  
(through the website) and e-mai l  the rest of the manuscript to the 
 associate editor.
On this occasion, instructions for the uploading procedure will be  
supplied.
The video cannot be changed or withdrawn once published in the  
Online First article. We propose that writers upload a new file to  
MOMO and get a  different identification code if they want to  
change the clip for any reason.
If you have any questions, please contact the head editor. Please  
remember that we do not allow any films depicting human  
conduct.
Conclusion
Many video applications rely on video abstraction, such as  
indexing, browsing, and retrieval.
The size of the keyframe set, representation scope, base unit, and  
underlying computational processes are all critical factors in  
keyframe extraction. Similarly, five key aspects are identified and  
explained for video skims the overall procedure, the duration of the 
 video skim, the working data domain, the underlying calculation  
method, and the characteristics employed
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