New Tampered Features for Scene and Caption Text Classification in Video Frame

2016 ◽  
Author(s):  
Sangheeta Roy ◽  
Palaiahnakote Shivakumara ◽  
Umapada Pal ◽  
Tong Lu ◽  
Chew Lim Tan
Keyword(s):  
Text Classification ◽  
Video Frame ◽  
Caption Text

A Video Semantic Mining Scheme

2011 ◽  
Vol 366 ◽  
pp. 64-67
Author(s):  
Shi Lin Zhang ◽  
He Ping Li ◽  
Shu Wu Zhang
Keyword(s):  
Object Recognition ◽  
Speech Recognition ◽  
Semantic Distance ◽  
Video Frame ◽  
Continuous Speech Recognition ◽  
Part Of Speech ◽  
Semantic Mining ◽  
Signal Vidéo ◽  
Mining Scheme ◽  
Caption Text

In this paper, we present a method for video semantic mining. Speech signal, video caption text and video frame images are all key factors for a person to understand the video content. Through above observation, we bring forward a method which integrating continuous speech recognition, video caption text recognition and object recognition. The video is firstly segmented to a serial of shots by shot detection. Then the caption text and speech recognition results are treated as two paragraphs of text. The object recognition results are presented by bag of words. The above three aspects of texts are processed by part of speech and stemming. Then only the noun words are kept. At last a video is represented by three bags of words. The words are further depicted as a graph. The graph vertices stand for the words and the edges denote the semantic distance between two neighboring words. In the last step, we apply the dense sub graph finding method to mine the video semantic meaning. Experiments show that our video semantic mining method is efficient.

Research on Edge Detection Algorithm for Caption Extraction from Video Frames

2012 ◽  
Vol 190-191 ◽  
pp. 1040-1043
Author(s):  
Jin Wu
Keyword(s):  
Edge Detection ◽  
Test Object ◽  
Video Retrieval ◽  
Detection Algorithm ◽  
Video Data ◽  
Experimental Results ◽  
Video Frame ◽  
Edge Detection Algorithm ◽  
Video Frames ◽  
Caption Text

Text area extraction from video caption has become an important tool for content-based video retrieval. Test object is frame data intercepted from video data. This paper proposes an algorithm of edge detection to extract caption text that embedded in the video frame by grayed processing. Experimental results have shown that the proposed approach is very effective in text area detection.

Video-microscopic measurement of cell-substratum traction forces generated by locomoting keratocytes

1993 ◽  
Vol 51 ◽  
pp. 188-189
Author(s):  
Tim Oliver ◽  
Michelle Leonard ◽  
Juliet Lee ◽  
Akira Ishihara ◽  
Ken Jacobson
Keyword(s):  
Silicone Rubber ◽  
Molecular Motors ◽  
Video Frame ◽  
Traction Forces ◽  
Cell Traction Forces ◽  
Latex Beads ◽  
Cell Traction ◽  
Local Cell ◽  
Microscopic Measurement ◽  
Kinetics Of

We are using video-enhanced light microscopy to investigate the pattern and magnitude of forces that fish keratocytes exert on flexible silicone rubber substrata. Our goal is a clearer understanding of the way molecular motors acting through the cytoskeleton co-ordinate their efforts into locomotion at cell velocities up to 1 μm/sec. Cell traction forces were previously observed as wrinkles(Fig.l) in strong silicone rubber films by Harris.(l) These forces are now measureable by two independant means.In the first of these assays, weakly crosslinked films are made, into which latex beads have been embedded.(Fig.2) These films report local cell-mediated traction forces as bead displacements in the plane of the film(Fig.3), which recover when the applied force is released. Calibrated flexible glass microneedles are then used to reproduce the translation of individual beads. We estimate the force required to distort these films to be 0.5 mdyne/μm of bead movement. Video-frame analysis of bead trajectories is providing data on the relative localisation, dissipation and kinetics of traction forces.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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