2,965 research outputs found
A Data-Driven Approach for Tag Refinement and Localization in Web Videos
Tagging of visual content is becoming more and more widespread as web-based
services and social networks have popularized tagging functionalities among
their users. These user-generated tags are used to ease browsing and
exploration of media collections, e.g. using tag clouds, or to retrieve
multimedia content. However, not all media are equally tagged by users. Using
the current systems is easy to tag a single photo, and even tagging a part of a
photo, like a face, has become common in sites like Flickr and Facebook. On the
other hand, tagging a video sequence is more complicated and time consuming, so
that users just tag the overall content of a video. In this paper we present a
method for automatic video annotation that increases the number of tags
originally provided by users, and localizes them temporally, associating tags
to keyframes. Our approach exploits collective knowledge embedded in
user-generated tags and web sources, and visual similarity of keyframes and
images uploaded to social sites like YouTube and Flickr, as well as web sources
like Google and Bing. Given a keyframe, our method is able to select on the fly
from these visual sources the training exemplars that should be the most
relevant for this test sample, and proceeds to transfer labels across similar
images. Compared to existing video tagging approaches that require training
classifiers for each tag, our system has few parameters, is easy to implement
and can deal with an open vocabulary scenario. We demonstrate the approach on
tag refinement and localization on DUT-WEBV, a large dataset of web videos, and
show state-of-the-art results.Comment: Preprint submitted to Computer Vision and Image Understanding (CVIU
Spatio-temporal Video Re-localization by Warp LSTM
The need for efficiently finding the video content a user wants is increasing
because of the erupting of user-generated videos on the Web. Existing
keyword-based or content-based video retrieval methods usually determine what
occurs in a video but not when and where. In this paper, we make an answer to
the question of when and where by formulating a new task, namely
spatio-temporal video re-localization. Specifically, given a query video and a
reference video, spatio-temporal video re-localization aims to localize
tubelets in the reference video such that the tubelets semantically correspond
to the query. To accurately localize the desired tubelets in the reference
video, we propose a novel warp LSTM network, which propagates the
spatio-temporal information for a long period and thereby captures the
corresponding long-term dependencies. Another issue for spatio-temporal video
re-localization is the lack of properly labeled video datasets. Therefore, we
reorganize the videos in the AVA dataset to form a new dataset for
spatio-temporal video re-localization research. Extensive experimental results
show that the proposed model achieves superior performances over the designed
baselines on the spatio-temporal video re-localization task
TagBook: A Semantic Video Representation without Supervision for Event Detection
We consider the problem of event detection in video for scenarios where only
few, or even zero examples are available for training. For this challenging
setting, the prevailing solutions in the literature rely on a semantic video
representation obtained from thousands of pre-trained concept detectors.
Different from existing work, we propose a new semantic video representation
that is based on freely available social tagged videos only, without the need
for training any intermediate concept detectors. We introduce a simple
algorithm that propagates tags from a video's nearest neighbors, similar in
spirit to the ones used for image retrieval, but redesign it for video event
detection by including video source set refinement and varying the video tag
assignment. We call our approach TagBook and study its construction,
descriptiveness and detection performance on the TRECVID 2013 and 2014
multimedia event detection datasets and the Columbia Consumer Video dataset.
Despite its simple nature, the proposed TagBook video representation is
remarkably effective for few-example and zero-example event detection, even
outperforming very recent state-of-the-art alternatives building on supervised
representations.Comment: accepted for publication as a regular paper in the IEEE Transactions
on Multimedi
Destination Image on the DMO's Platforms: Official Website and Social Media
Tourists usually configure and develop ideas about possible destinations based on information previously gathered from both social media and the official web of the destination management organization (DMO). In spite of the relevance of said information sources, there have not been many studies evaluating how these different sources influence the destination image. This research proposes a model intended to explain the image creation process of a destination taking into account both the DMO’s online platforms and the perceived psychological distance. The proposed model is tested with an empirical study including a questionnaire which collects data from 264 participants. The validity of the model is reviewed through PLS analysis. Results show that the psychological distance does not influence the overall destination image. In addition, the overall destination image can be estimated to a larger extent when tourists approach social media as their main source of information. Implications and conclusions are discussed.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
Learning to Localize and Align Fine-Grained Actions to Sparse Instructions
Automatic generation of textual video descriptions that are time-aligned with
video content is a long-standing goal in computer vision. The task is
challenging due to the difficulty of bridging the semantic gap between the
visual and natural language domains. This paper addresses the task of
automatically generating an alignment between a set of instructions and a first
person video demonstrating an activity. The sparse descriptions and ambiguity
of written instructions create significant alignment challenges. The key to our
approach is the use of egocentric cues to generate a concise set of action
proposals, which are then matched to recipe steps using object recognition and
computational linguistic techniques. We obtain promising results on both the
Extended GTEA Gaze+ dataset and the Bristol Egocentric Object Interactions
Dataset
- …