Video Mining for Anomaly Examining and Mishap Scrutinize

2020 ◽  
Author(s):  
D LOKESH SAIKUMAR
Keyword(s):  
Video Mining

scholarly journals A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining

Algorithms ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 97
Author(s):  
Lin Tang ◽  
Lin Liu ◽  
Jianhou Gan
Keyword(s):  
Real Time ◽  
Gibbs Sampling ◽  
Topic Model ◽  
Video Mining

Automatic in vivo microscopy video mining for leukocytes

2007 ◽  
Vol 9 (1) ◽  
pp. 30-37 ◽  
Author(s):  
Chengcui Zhang ◽  
Wei-Bang Chen ◽  
Lin Yang ◽  
Xin Chen ◽  
John K. Johnstone
Keyword(s):  
In Vivo Microscopy ◽  
Video Mining

Event Detection in Sports Video Based on Generative-Discriminative Models

2011 ◽  
pp. 143-165
Author(s):  
Guoliang Fan ◽  
Yi Ding
Keyword(s):  
Event Detection ◽  
Semantic Analysis ◽  
Building Blocks ◽  
Semantic Space ◽  
Sports Video ◽  
Low Level ◽  
Discriminative Models ◽  
Video Mining ◽  
High Level ◽  
Different Levels

Semantic event detection is an active and interesting research topic in the field of video mining. The major challenge is the semantic gap between low-level features and high-level semantics. In this chapter, we will advance a new sports video mining framework where a hybrid generative-discriminative approach is used for event detection. Specifically, we propose a three-layer semantic space by which event detection is converted into two inter-related statistical inference procedures that involve semantic analysis at different levels. The first is to infer the mid-level semantic structures from the low-level visual features via generative models, which can serve as building blocks of high-level semantic analysis. The second is to detect high-level semantics from mid-level semantic structures using discriminative models, which are of direct interests to users. In this framework we can explicitly represent and detect semantics at different levels. The use of generative and discriminative approaches in two different stages is proved to be effective and appropriate for event detection in sports video. The experimental results from a set of American football video data demonstrate that the proposed framework offers promising results compared with traditional approaches.

scholarly journals Image and video mining through online learning

2017 ◽  
Vol 158 ◽  
pp. 72-84 ◽  
Author(s):  
Andrew Gilbert ◽  
Richard Bowden
Keyword(s):  
Online Learning ◽  
Video Mining

Video mining using combinations of unsupervised and supervised learning techniques

2003 ◽  
Author(s):  
Ajay Divakaran ◽  
Koji Miyahara ◽  
Kadir A. Peker ◽  
Regunathan Radhakrishnan ◽  
Ziyou Xiong
Keyword(s):  
Supervised Learning ◽  
Video Mining ◽  
Learning Techniques

Manycore processor for video mining applications

2013 ◽  
Author(s):  
Y. Matsumoto ◽  
H. Uchida ◽  
M. Hagimoto ◽  
Y. Hibi ◽  
S. Torii ◽  
...  
Keyword(s):  
Video Mining
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