3,410 research outputs found
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
A framework for automatic semantic video annotation
The rapidly increasing quantity of publicly available videos has driven research into developing automatic tools for indexing, rating, searching and retrieval. Textual semantic representations, such as tagging, labelling and annotation, are often important factors in the process of indexing any video, because of their user-friendly way of representing the semantics appropriate for search and retrieval. Ideally, this annotation should be inspired by the human cognitive way of perceiving and of describing videos. The difference between the low-level visual contents and the corresponding human perception is referred to as the âsemantic gapâ. Tackling this gap is even harder in the case of unconstrained videos, mainly due to the lack of any previous information about the analyzed video on the one hand, and the huge amount of generic knowledge required on the other. This paper introduces a framework for the Automatic Semantic Annotation of unconstrained videos. The proposed framework utilizes two non-domain-specific layers: low-level visual similarity matching, and an annotation analysis that employs commonsense knowledgebases. Commonsense ontology is created by incorporating multiple-structured semantic relationships. Experiments and black-box tests are carried out on standard video databases for action recognition and video information retrieval. White-box tests examine the performance of the individual intermediate layers of the framework, and the evaluation of the results and the statistical analysis show that integrating visual similarity matching with commonsense semantic relationships provides an effective approach to automated video annotation
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and âenablersâ, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
AudioPairBank: Towards A Large-Scale Tag-Pair-Based Audio Content Analysis
Recently, sound recognition has been used to identify sounds, such as car and
river. However, sounds have nuances that may be better described by
adjective-noun pairs such as slow car, and verb-noun pairs such as flying
insects, which are under explored. Therefore, in this work we investigate the
relation between audio content and both adjective-noun pairs and verb-noun
pairs. Due to the lack of datasets with these kinds of annotations, we
collected and processed the AudioPairBank corpus consisting of a combined total
of 1,123 pairs and over 33,000 audio files. One contribution is the previously
unavailable documentation of the challenges and implications of collecting
audio recordings with these type of labels. A second contribution is to show
the degree of correlation between the audio content and the labels through
sound recognition experiments, which yielded results of 70% accuracy, hence
also providing a performance benchmark. The results and study in this paper
encourage further exploration of the nuances in audio and are meant to
complement similar research performed on images and text in multimedia
analysis.Comment: This paper is a revised version of "AudioSentibank: Large-scale
Semantic Ontology of Acoustic Concepts for Audio Content Analysis
Semantic multimedia analysis using knowledge and context
PhDThe difficulty of semantic multimedia analysis can be attributed to the
extended diversity in form and appearance exhibited by the majority of
semantic concepts and the difficulty to express them using a finite number
of patterns. In meeting this challenge there has been a scientific debate
on whether the problem should be addressed from the perspective of using
overwhelming amounts of training data to capture all possible instantiations
of a concept, or from the perspective of using explicit knowledge about
the conceptsâ relations to infer their presence. In this thesis we address
three problems of pattern recognition and propose solutions that combine
the knowledge extracted implicitly from training data with the knowledge
provided explicitly in structured form. First, we propose a BNs modeling
approach that defines a conceptual space where both domain related evi-
dence and evidence derived from content analysis can be jointly considered
to support or disprove a hypothesis. The use of this space leads to sig-
nificant gains in performance compared to analysis methods that can not
handle combined knowledge. Then, we present an unsupervised method
that exploits the collective nature of social media to automatically obtain
large amounts of annotated image regions. By proving that the quality of
the obtained samples can be almost as good as manually annotated images
when working with large datasets, we significantly contribute towards scal-
able object detection. Finally, we introduce a method that treats images,
visual features and tags as the three observable variables of an aspect model
and extracts a set of latent topics that incorporates the semantics of both
visual and tag information space. By showing that the cross-modal depen-
dencies of tagged images can be exploited to increase the semantic capacity
of the resulting space, we advocate the use of all existing information facets
in the semantic analysis of social media
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
Visual multimedia have become an inseparable part of our digital social
lives, and they often capture moments tied with deep affections. Automated
visual sentiment analysis tools can provide a means of extracting the rich
feelings and latent dispositions embedded in these media. In this work, we
explore how Convolutional Neural Networks (CNNs), a now de facto computational
machine learning tool particularly in the area of Computer Vision, can be
specifically applied to the task of visual sentiment prediction. We accomplish
this through fine-tuning experiments using a state-of-the-art CNN and via
rigorous architecture analysis, we present several modifications that lead to
accuracy improvements over prior art on a dataset of images from a popular
social media platform. We additionally present visualizations of local patterns
that the network learned to associate with image sentiment for insight into how
visual positivity (or negativity) is perceived by the model.Comment: Accepted for publication in Image and Vision Computing. Models and
source code available at https://github.com/imatge-upc/sentiment-201
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