Back to Search
Start Over
Substantial Content Reclamation for Clustering
- Source :
- International Journal of Recent Technology and Engineering (IJRTE). 10:17-20
- Publication Year :
- 2021
- Publisher :
- Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, 2021.
-
Abstract
- The massive volume of data stored in computer files and databases is rapidly increasing. Users of these data, on the other hand, demand more complex information from databases. The video data have exponential growth towards accessing and storing. The vital problem associated to video data is efficient, qualitative and fast accessing. We talk about how video pictures are clustered. We presume video clips have been divided into shots, each of which is denoted by a collection of key frames. As a result, video clustering is limited to still key frame pictures. In amble database finding the qualified data set (clusters) is quite time-taking job. The video data mining relate to multiālingual text, numeric, image, video, audio, graphical, temporal, relational and categorical data. It may be any kind of information medium that can be represented, processed, stored, fast accessing or summarization of clusters are required due to which significant frame-set is formed. Due to sampling error and test reliability in video, substantial changes of more than one frame are predicted. The goal of this article is to show how to employ a familiar and easy nonparametric statistical approach (chi-square) to select eligible data/framesets for analysis. The chi-square model illustrated here is a straightforward, sensible, fast, reduce saddle, and easiest method. Skimming/ Summarization and clipping technique are further enhanced by this technique along with video database maintenance technique from simple descriptors to a complex description schemes like spatial and temporal or high dimensional indexing.&nbsp
- Subjects :
- business.industry
Computer science
2277-3878
Data mining, Clusters, Chi-square, Non-Parametric, Skimming, Text Mining
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
General Engineering
Nonparametric statistics
computer.software_genre
100.1/ijrte.C63650910321
Text mining
Land reclamation
Management of Technology and Innovation
Content (measure theory)
Data mining
business
Cluster analysis
computer
Subjects
Details
- ISSN :
- 22773878
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- International Journal of Recent Technology and Engineering (IJRTE)
- Accession number :
- edsair.doi.dedup.....ee6b4361cab5114b8336b27c69ef8f47