NDLI: Zero-shot video retrieval using content and concepts

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Zero-shot video retrieval using content and concepts

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Zero-shot video retrieval using content and concepts

Content Provider	ACM Digital Library
Author	Dalton, Jeffrey Allan, James Mirajkar, Pranav
Abstract	Recent research in video retrieval has been successful at finding videos when the query consists of tens or hundreds of sample relevant videos for training supervised models. Instead, we investigate unsupervised zero-shot retrieval where no training videos are provided: a query consists only of a text statement. For retrieval, we use text extracted from images in the videos, text recognized in the speech of its audio track, as well as automatically detected semantically meaningful visual video concepts identified with widely varying confidence in the videos. In this work we introduce a new method for automatically identifying relevant concepts given a text query using the Markov Random Field (MRF) retrieval framework. We use source expansion to build rich textual representations of semantic video concepts from large external sources such as the web. We find that concept-based retrieval significantly outperforms text based approaches in recall. Using an evaluation derived from the TRECVID MED'11 track, we present early results that an approach using multi-modal fusion can compensate for inadequacies in each modality, resulting in substantial effectiveness gains. With relevance feedback, our approach provides additional improvements of over 50%.
Starting Page	1857
Ending Page	1860
Page Count	4
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2507880
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Video retrieval Information retrieval Information extraction
Content Type	Text
Resource Type	Article

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