14 January 2012 Efficient indexing and searching framework for unstructured data
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Proceedings Volume 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis; 83493F (2012); doi: 10.1117/12.921130
Event: Fourth International Conference on Machine Vision (ICMV 11), 2011, Singapore, Singapore
Abstract
The proliferation of unstructured data continues to grow within organizations of all types. This data growth has introduced the key question of how we effectively find and manage them in the growing sea of information. As a result, there has been an increasing demand for efficient search on them. Providing effective indexing and search on unstructured data is not a simple task. Unstructured data include documents, images, audio, video and so on. In this paper, we propose an efficient indexing and searching framework for unstructured data. In this framework, text-based and content-based approaches are incorporated for unstructured data retrieval. Our retrieval framework can support various types of queries and can accept multimedia examples and metadata-based documents. The aim of this paper is to use various features of multimedia data and to make content-based multimedia retrieval system more efficient.
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Kyar Nyo Aye, Ni Lar Thein, "Efficient indexing and searching framework for unstructured data", Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 83493F (14 January 2012); doi: 10.1117/12.921130; https://doi.org/10.1117/12.921130
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KEYWORDS
Multimedia

Video

Feature extraction

RGB color model

Data conversion

Image processing

Databases

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