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Visual Semantic Based 3D Video Retrieval System Using HDFS.

C Ranjith Kumar1, S Suguna2

  • 1Bharathiar University, Coimbatore, Coimbatore District, cranjithkumarscholar@gmail.com.

Data Mining and Knowledge Discovery
|December 23, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel 3D video search system using combined features for enhanced retrieval. The system efficiently handles large datasets, improving accuracy and reducing processing time for 3D content-based video retrieval.

Keywords:
Bag Of Visual WordsHadoop Distributed File SystemPredictive Clustering Treelocal descriptorsvideo retrieval

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Area of Science:

  • Computer Science
  • Multimedia Systems
  • Information Retrieval

Background:

  • Traditional video retrieval often relies on limited features.
  • 3D video content presents unique challenges for search and analysis.
  • Existing methods may not effectively integrate diverse features for 3D retrieval.

Purpose of the Study:

  • To propose a novel framework for visual semantic-based 3D video search and retrieval.
  • To explore the application of Bag of Visual Words (BOVW) and MapReduce in a 3D context.
  • To enhance 3D Content-Based Video Retrieval (3D-CBVR) by integrating multiple feature types.

Main Methods:

  • Feature extraction combining geometric, topological, and 3D co-occurrence matrix features for shape, color, and texture.
  • Utilizing the Threshold Based-Predictive Clustering Tree (TB-PCT) algorithm for visual codebook generation.
  • Implementing a soft weighting scheme with L2 distance for matching and ranking results based on an Index value.

Main Results:

  • The proposed system demonstrates meticulous results in 3D video search and retrieval.
  • Integration of HDFS (Hadoop Distributed File System) ensures efficient handling of large datasets.
  • The system significantly reduces the time complexity associated with 3D video retrieval.

Conclusions:

  • The novel framework offers a significant advancement in 3D video search and retrieval applications.
  • The integration of diverse features and efficient data handling provides a robust solution.
  • This approach paves the way for more effective 3D-CBVR systems.