Efficient Indexing and Searching Framework for Unstructured Data

被引：0

作者：

Aye, Kyar Nyo ^{[1
]}

Thein, Ni Lar ^{[1
]}

机构：

[1] Univ Comp Studies, Yangon, Myanmar

来源：

FOURTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2011): MACHINE VISION, IMAGE PROCESSING, AND PATTERN ANALYSIS | 2012年 / 8349卷

关键词：

unstructured data; indexing; content-based retrieval;

D O I：

10.1117/12.921130

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：5