Multimodal Indexing of Multilingual News VideoReport as inadecuate




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International Journal of Digital Multimedia BroadcastingVolume 2010 2010, Article ID 486487, 18 pages

Research Article

TCS Innovation Labs Delhi, TCS Towers, 249 DandE Udyog Vihar Phase IV, Gurgaon 122015, India

TCS Innovation Labs Mumbai, Yantra Park, Pokhran Road no. 2, Thane West 400601, India

TCS Innovation Labs Kolkata, Plot A2, M2-N2 Sector 5, Block GP, Salt Lake Electronics Complex, Kolkata 700091, India

Received 16 September 2009; Revised 27 December 2009; Accepted 2 March 2010

Academic Editor: Ling Shao

Copyright © 2010 Hiranmay Ghosh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The problems associated with automatic analysis of news telecasts are more severe in a country like India, where there are many national and regional language channels, besides English. In this paper, we present a framework for multimodal analysis of multilingual news telecasts, which can be augmented with tools and techniques for specific news analytics tasks. Further, we focus on a set of techniques for automatic indexing of the news stories based on keywords spotted in speech as well as on the visuals of contemporary and domain interest. English keywords are derived from RSS feed and converted to Indian language equivalents for detection in speech and on ticker texts. Restricting the keyword list to a manageable number results in drastic improvement in indexing performance. We present illustrative examples and detailed experimental results to substantiate our claim.





Author: Hiranmay Ghosh, Sunil Kumar Kopparapu, Tanushyam Chattopadhyay, Ashish Khare, Sujal Subhash Wattamwar, Amarendra Gorai, and

Source: https://www.hindawi.com/



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