scholarly journals A STUDY OF ACTOR AND ACTION SEMANTIC RETENTION IN VIDEO SUPERVOXEL SEGMENTATION

2013 ◽  
Vol 07 (04) ◽  
pp. 353-375 ◽  
Author(s):  
CHENLIANG XU ◽  
RICHARD F. DOELL ◽  
STEPHEN JOSÉ HANSON ◽  
CATHERINE HANSON ◽  
JASON J. CORSO
Keyword(s):  
Large Scale ◽  
Human Perception ◽  
Large Set ◽  
Video Content ◽  
Shape Context ◽  
Large Scale Data ◽  
Human Perceptions ◽  
New Feature ◽  
Large Scale Data Processing ◽  
High Level

Existing methods in the semantic computer vision community seem unable to deal with the explosion and richness of modern, open-source and social video content. Although sophisticated methods such as object detection or bag-of-words models have been well studied, they typically operate on low level features and ultimately suffer from either scalability issues or a lack of semantic meaning. On the other hand, video supervoxel segmentation has recently been established and applied to large scale data processing, which potentially serves as an intermediate representation to high level video semantic extraction. The supervoxels are rich decompositions of the video content: they capture object shape and motion well. However, it is not yet known if the supervoxel segmentation retains the semantics of the underlying video content. In this paper, we conduct a systematic study of how well the actor and action semantics are retained in video supervoxel segmentation. Our study has human observers watching supervoxel segmentation videos and trying to discriminate both actor (human or animal) and action (one of eight everyday actions). We gather and analyze a large set of 640 human perceptions over 96 videos in 3 different supervoxel scales. Furthermore, we design a feature defined on supervoxel segmentation, called supervoxel shape context, which is inspired by the higher order processes in human perception. We conduct actor and action classification experiments with this new feature and compare to various traditional video features. Our ultimate findings suggest that a significant amount of semantics have been well retained in the video supervoxel segmentation and can be used for further video analysis.

Advanced monitoring techniques for a large‐scale data‐processing network

2008 ◽  
Vol 25 (5) ◽  
pp. 287-300 ◽  
Author(s):  
B. Martin ◽  
A. Al‐Shabibi ◽  
S.M. Batraneanu ◽  
Ciobotaru ◽  
G.L. Darlea ◽  
...  
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Monitoring Techniques ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Processing Network ◽  
Scale Data

BestPeer++: A Peer-to-Peer Based Large-Scale Data Processing Platform

2014 ◽  
Vol 26 (6) ◽  
pp. 1316-1331 ◽  
Author(s):  
Gang Chen ◽  
Tianlei Hu ◽  
Dawei Jiang ◽  
Peng Lu ◽  
Kian-Lee Tan ◽  
...  
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Peer To Peer ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Processing Platform ◽  
Scale Data

scholarly journals Medimate : Ailment Diffusion Control System with Real Time Large Scale Data Processing

2018 ◽  
Vol 7 (2.31) ◽  
pp. 240
Author(s):  
S Sujeetha ◽  
Veneesa Ja ◽  
K Vinitha ◽  
R Suvedha
Keyword(s):  
Control System ◽  
Data Processing ◽  
Real Time ◽  
Large Scale ◽  
Diffusion Control ◽  
Qr Code ◽  
Healthcare Applications ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Scale Data

In the existing scenario, a patient has to go to the hospital to take necessary tests, consult a doctor and buy prescribed medicines or use specified healthcare applications. Hence time is wasted at hospitals and in medical shops. In the case of healthcare applications, face to face interaction with the doctor is not available. The downside of the existing scenario can be improved by the Medimate: Ailment diffusion control system with real time large scale data processing. The purpose of medimate is to establish a Tele Conference Medical System that can be used in remote areas. The medimate is configured for better diagnosis and medical treatment for the rural people. The system is installed with Heart Beat Sensor, Temperature Sensor, Ultrasonic Sensor and Load Cell to monitor the patient’s health parameters. The voice instructions are updated for easier access.  The application for enabling video and voice communication with the doctor through Camera and Headphone is installed at both the ends. The doctor examines the patient and prescribes themedicines. The medical dispenser delivers medicine to the patient as per the prescription. The QR code will be generated for each prescription by medimate and that QR code can be used forthe repeated medical conditions in the future. Medical details are updated in the server periodically.  

Pattern Recognition for Large-Scale Data Processing

2015 ◽  
pp. 198-208 ◽  
Author(s):  
Amir Basirat ◽  
Asad I. Khan ◽  
Heinz W. Schmidt
Keyword(s):  
Large Scale ◽  
Distributed Processing ◽  
Data Sets ◽  
Distributed Data ◽  
Time Data ◽  
Deterministic Learning ◽  
Large Scale Data ◽  
Future Data ◽  
Large Scale Data Processing ◽  
Learning Schemes

One of the main challenges for large-scale computer clouds dealing with massive real-time data is in coping with the rate at which unprocessed data is being accumulated. Transforming big data into valuable information requires a fundamental re-think of the way in which future data management models will need to be developed on the Internet. Unlike the existing relational schemes, pattern-matching approaches can analyze data in similar ways to which our brain links information. Such interactions when implemented in voluminous data clouds can assist in finding overarching relations in complex and highly distributed data sets. In this chapter, a different perspective of data recognition is considered. Rather than looking at conventional approaches, such as statistical computations and deterministic learning schemes, this chapter focuses on distributed processing approach for scalable data recognition and processing.

Essentiality of Machine Learning Algorithms for Big Data Computation

2016 ◽  
pp. 156-167
Author(s):  
Manjunath Thimmasandra Narayanapppa ◽  
T. P. Puneeth Kumar ◽  
Ravindra S. Hegadi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Scale ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Computational Environment ◽  
Large Scale Data Processing

Recent technological advancements have led to generation of huge volume of data from distinctive domains (scientific sensors, health care, user-generated data, finical companies and internet and supply chain systems) over the past decade. To capture the meaning of this emerging trend the term big data was coined. In addition to its huge volume, big data also exhibits several unique characteristics as compared with traditional data. For instance, big data is generally unstructured and require more real-time analysis. This development calls for new system platforms for data acquisition, storage, transmission and large-scale data processing mechanisms. In recent years analytics industries interest expanding towards the big data analytics to uncover potentials concealed in big data, such as hidden patterns or unknown correlations. The main goal of this chapter is to explore the importance of machine learning algorithms and computational environment including hardware and software that is required to perform analytics on big data.

Integration of large-scale data processing systems and traditional parallel database technology

2019 ◽  
Vol 12 (12) ◽  
pp. 2290-2299
Author(s):  
Azza Abouzied ◽  
Daniel J. Abadi ◽  
Kamil Bajda-Pawlikowski ◽  
Avi Silberschatz
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Parallel Database ◽  
Large Scale Data ◽  
Database Technology ◽  
Large Scale Data Processing ◽  
Scale Data
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