Asymmetric Spatio-Temporal Embeddings for Large-Scale Image-to-Video Retrieval

Noa Garcia , George Vogiatzis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We address the problem of image-to-video retrieval. Given a query image, the aim
is to identify the frame or scene within a collection of videos that best matches the visual
input. Matching images to videos is an asymmetric task in which specific features
for capturing the visual information in images and, at the same time, compacting the
temporal correlation from videos are needed. Methods proposed so far are based on the
temporal aggregation of hand-crafted features. In this work, we propose a deep learning
architecture for learning specific asymmetric spatio-temporal embeddings for image-tovideo
retrieval. Our method learns non-linear projections from training data for both
images and videos and projects their visual content into a common latent space, where
they can be easily compared with a standard similarity function. Experiments conducted
here show that our proposed asymmetric spatio-temporal embeddings outperform stateof-the-art
in standard image-to-video retrieval datasets.
Original languageEnglish
Title of host publication29TH BRITISH MACHINE VISION CONFERENCE
Publication statusPublished - 6 Sep 2018
Event29TH BRITISH MACHINE VISION CONFERENCE - Newcastle, United Kingdom
Duration: 3 Sep 20186 Sep 2018

Conference

Conference29TH BRITISH MACHINE VISION CONFERENCE
CountryUnited Kingdom
CityNewcastle
Period3/09/186/09/18

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Bibliographical note

© 2018. The copyright of this document resides with its authors.
It may be distributed unchanged freely in print or electronic forms

Cite this

Garcia , N., & Vogiatzis, G. (2018). Asymmetric Spatio-Temporal Embeddings for Large-Scale Image-to-Video Retrieval. In 29TH BRITISH MACHINE VISION CONFERENCE