Image from Google Jackets

Multimodal Location Estimation of Videos and Images / edited by Jaeyoung Choi, Gerald Friedland.

Contributor(s): Choi, Jaeyoung., editor literario | Friedland, Gerald., editor literario
Material type: materialTypeLabelE-bookSeries: (Engineering (Springer-11647)).Publisher: Cham : Springer International Publishing, 2015Description: 1 recurso en línea (XII, 191 páginas 80 ilustraciones a color).ISBN: 9783319098616.Subject: Localización (Informática) | Proceso digital de imágenesOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Introduction -- The Benchmark as a Research Catalyst: Charting the Progress of Geo-Prediction for Social Multimedia -- Large-scale Image Geolocalization -- Vision-based Fine-Grained Location Estimation -- Image-Based Positioning of Mobile Devices in Indoor Environments -- Application of Large-Scale Classification Techniques for Simple Location Estimation Experiments -- Collaborative Multimodal Location Estimation of Consumer Media -- Georeferencing Flickr resources based on multimodal features -- Human vs Machine: Establishing a Human Baseline for Multimodal Location Estimation -- Personalized Travel Navigation and Photo-Shooting Navigation Using Large-Scale Geotags.
Abstract: This book presents an overview of the field of multimodal location estimation, i.e. using acoustic, visual, and/or textual cues to estimate the shown location of a video recording. The authors' sample research results in this field in a unified way integrating research work on this topic that focuses on different modalities, viewpoints, and applications. The book describes fundamental methods of acoustic, visual, textual, social graph, and metadata processing as well as multimodal integration methods used for location estimation. In addition, the text covers benchmark metrics and explores the limits of the technology based on a human baseline. ·         Discusses localization of multimedia data; ·         Examines fundamental methods of establishing location metadata for images and videos (other than GPS tagging); ·         Covers Data-Driven as well as Semantic Location Estimation.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería TK5105.65 M858 2015 EB (Browse shelf(Opens below)) Acceso electrónico eBook.12112252
Total holds: 0

Introduction -- The Benchmark as a Research Catalyst: Charting the Progress of Geo-Prediction for Social Multimedia -- Large-scale Image Geolocalization -- Vision-based Fine-Grained Location Estimation -- Image-Based Positioning of Mobile Devices in Indoor Environments -- Application of Large-Scale Classification Techniques for Simple Location Estimation Experiments -- Collaborative Multimodal Location Estimation of Consumer Media -- Georeferencing Flickr resources based on multimodal features -- Human vs Machine: Establishing a Human Baseline for Multimodal Location Estimation -- Personalized Travel Navigation and Photo-Shooting Navigation Using Large-Scale Geotags.

This book presents an overview of the field of multimodal location estimation, i.e. using acoustic, visual, and/or textual cues to estimate the shown location of a video recording. The authors' sample research results in this field in a unified way integrating research work on this topic that focuses on different modalities, viewpoints, and applications. The book describes fundamental methods of acoustic, visual, textual, social graph, and metadata processing as well as multimodal integration methods used for location estimation. In addition, the text covers benchmark metrics and explores the limits of the technology based on a human baseline. ·         Discusses localization of multimedia data; ·         Examines fundamental methods of establishing location metadata for images and videos (other than GPS tagging); ·         Covers Data-Driven as well as Semantic Location Estimation.

There are no comments on this title.

to post a comment.