Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Volume-3 | Issue-3
Construction of Efficient Smart Voting Machine with Liveness Detection Module
Volume-3 | Issue-3
An Economical Robotic Arm for Playing Chess Using Visual Servoing
Volume-2 | Issue-3
Triplet loss for Chromosome Classification
Volume-4 | Issue-1
Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
Volume-3 | Issue-4
Real Time Sign Language Recognition and Speech Generation
Volume-2 | Issue-2
Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1
Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 5 | Issue - 2 | june 2023
Published
12 June, 2023
As technology progresses, a gigantic amount of video data is generated day-by-day. Processing of such a huge video requires time, increased storage, and a computational power. Sometimes it is convenient for the user to watch a summary or highlight rather than watching a complete video, which is a time-consuming task. So, a fully automated solution is required to extract important segments from video. Researchers have proposed multiple approaches / techniques for summarizing the videos which resolve the problem of long videos and summarize them according to the video type. This survey and comparative evaluation of video summarizing techniques based on several domains are presented in this study. Primarily, these methods are classified into different categories based on their methods or techniques used. Furthermore, an overview of some of the latest literature is presented with the dataset and the evaluation approaches used. The review is also made related to the domain direction, and is concluded by presenting the benefits and difficulties associated with the current video summarization techniques.
KeywordsVideo summarization video skimming static summarization key frame Summary sequence
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