scholarly journals High-Dimensional Indexing for Video Retrieval

10.5772/37303 ◽  
2012 ◽  
Author(s):  
Catalin Calistru ◽  
Cristina Ribeiro ◽  
Gabriel Davi
Keyword(s):  
Video Retrieval ◽  
High Dimensional ◽  
High Dimensional Indexing

scholarly journals Scalable high-dimensional indexing with Hadoop

2013 ◽  
Author(s):  
Denis Shestakov ◽  
Diana Moise ◽  
Gylfi Gudmundsson ◽  
Laurent Amsaleg
Keyword(s):  
High Dimensional ◽  
High Dimensional Indexing

An efficient high-dimensional indexing method for content-based retrieval in large image databases

2009 ◽  
Vol 24 (10) ◽  
pp. 775-790 ◽  
Author(s):  
I. Daoudi ◽  
K. Idrissi ◽  
S.E. Ouatik ◽  
A. Baskurt ◽  
D. Aboutajdine
Keyword(s):  
Image Databases ◽  
High Dimensional ◽  
Content Based Retrieval ◽  
Indexing Method ◽  
High Dimensional Indexing ◽  
Large Image Databases

Extending High-Dimensional Indexing Techniques Pyramid and iMinMax(θ): Lessons Learned

2013 ◽  
pp. 253-267 ◽  
Author(s):  
Karthik Ganesan Pillai ◽  
Liessman Sturlaugson ◽  
Juan M. Banda ◽  
Rafal A. Angryk
Keyword(s):  
Lessons Learned ◽  
High Dimensional ◽  
Indexing Techniques ◽  
High Dimensional Indexing

Building High Dimensional Indexing Structure Based on Cluster Information for Better Space Utilization

2006 ◽  
Vol 5 (6) ◽  
pp. 1038-1042
Author(s):  
Sanjay Garg ◽  
Ramesh Chandra Jain .
Keyword(s):  
High Dimensional ◽  
Space Utilization ◽  
Indexing Structure ◽  
High Dimensional Indexing

Design by a CBIR System Supporting High Level Concepts

2011 ◽  
pp. 259-268
Author(s):  
M. V. Ramakrishna ◽  
S. Nepal ◽  
S. Sumanasekara ◽  
S. M.M. Tahaghoghi
Keyword(s):  
Similarity Measures ◽  
Linear Mapping ◽  
Content Based Image Retrieval ◽  
High Dimensional ◽  
Test Bed ◽  
Development Activity ◽  
Indexing Structure ◽  
Level Data ◽  
High Dimensional Indexing ◽  
High Level

Content Based Image Retrieval (CBIR) systems that are able to “retrieve images of Clinton with Lewinsky” are unrealistic at present. However, this area has seen much research and development activity since IBM’s QBIC announcement in 1994. The CHITRA CBIR system under development at the RMIT and Monash Universities, addresses the need for a test bed system. Users can dynamically incorporate new features and similarity measures in to the system, enabling it to act as a testbed for CBIR research. The system uses a 4-level data model we have developed and supports definition and querying of high level concepts such as MOUNTAIN and SUNSET. These advanced capabilities are supported by a powerful graphical query mechanism and a high-dimensional indexing structure based on linear mapping. In this paper we describe the design of the system, our contributions to the state of the art and provide some implementation details.

scholarly journals Shots Temporal Prediction Rules for High-Dimensional Data of Semantic Video Retrieval

2018 ◽  
Vol 15 (1) ◽  
pp. 60-69
Author(s):  
Shaimaa Toriah Mohamed Toriah ◽  
Atef Zaki Ghalwash ◽  
Aliaa A.A. Youssif
Keyword(s):  
Video Retrieval ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Prediction Rules ◽  
Temporal Prediction
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