Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F15%3A00224288" target="_blank" >RIV/68407700:21240/15:00224288 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-14998-1_13" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-14998-1_13</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-14998-1_13" target="_blank" >10.1007/978-3-319-14998-1_13</a>

Result language
angličtina
Original language name
Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video
Original language description
Visual concept detection is one of the most active research areas in multimedia analysis. The goal of visual concept detection is to assign to each elementary temporal segment of a video, a confidence score for each target concept (e.g. forest, ocean, sky, etc.). The establishment of such associations between the video content and the concept labels is a key step toward semantics-based indexing, retrieval, and summarization of videos, as well as deeper analysis (e.g., video event detection). Due to itssignificance for the multimedia analysis community, concept detection is the topic of international benchmarking activities such as TRECVID. While video is typically a multi-modal signal composed of visual content, speech, audio, and possibly also subtitles, most research has so far focused on exploiting the visual modality. In this chapter we introduce fusion and text analysis techniques for harnessing automatic speech recognition (ASR) transcripts or subtitles for improving the results
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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