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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

C - Kapitola v odborné knize

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Multimedia Data Mining and Analytics

ISBN

978-3-319-14997-4

Počet stran výsledku

16

Strana od-do

295-310

Počet stran knihy

454

Název nakladatele

Springer International Publishing AG

Místo vydání

Cham

Kód UT WoS kapitoly

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