7,739 research outputs found
Automatic semantic video annotation in wide domain videos based on similarity and commonsense knowledgebases
In this paper, we introduce a novel framework for automatic Semantic Video Annotation. As this framework detects possible events occurring in video clips, it forms the annotating base of video search engine. To achieve this purpose, the system has to able to operate on uncontrolled wide-domain videos. Thus, all layers have to be based on generic features.
This framework aims to bridge the "semantic gap", which is the difference between the low-level visual features and the human's perception, by finding videos with similar visual events, then analyzing their free text annotation to find a common area then to decide the best description for this new video using commonsense knowledgebases.
Experiments were performed on wide-domain video clips from the TRECVID 2005 BBC rush standard database. Results from these experiments show promising integrity between those two layers in order to find expressing annotations for the input video. These results were evaluated based on retrieval performance
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 ‘semantic gap’. 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
VisualNet: Commonsense knowledgebase for video and image indexing and retrieval application
The rapidly increasing amount of video collections, available on the web or via broadcasting, motivated research towards building intelligent tools for searching, rating, indexing and retrieval purposes. Establishing a semantic representation of visual data, mainly in textual form, is one of the important tasks. The time needed for building and maintaining Ontologies and knowledge, especially for wide domain, and the efforts for integrating several approaches emphasize the need for unified generic commonsense knowledgebase for visual applications. In this paper, we propose a novel commonsense knowledgebase that forms the link between the visual world and its semantic textual representation. We refer to it as "VisualNet". VisualNet is obtained by our fully automated engine that constructs a new unified structure concluding the knowledge from two commonsense knowledgebases, namely WordNet and ConceptNet. This knowledge is extracted by performing analysis operations on WordNet and ConceptNet contents, and then only useful knowledge in visual domain applications is considered. Moreover, this automatic engine enables this knowledgebase to be developed, updated and maintained automatically, synchronized with any future enhancement on WordNet or ConceptNet. Statistical properties of the proposed knowledgebase, in addition to an evaluation of a sample application results, show coherency and effectiveness of the proposed knowledgebase and its automatic engine
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
Despite the recent advances in opinion mining for written reviews, few works
have tackled the problem on other sources of reviews. In light of this issue,
we propose a multi-modal approach for mining fine-grained opinions from video
reviews that is able to determine the aspects of the item under review that are
being discussed and the sentiment orientation towards them. Our approach works
at the sentence level without the need for time annotations and uses features
derived from the audio, video and language transcriptions of its contents. We
evaluate our approach on two datasets and show that leveraging the video and
audio modalities consistently provides increased performance over text-only
baselines, providing evidence these extra modalities are key in better
understanding video reviews.Comment: Second Grand Challenge and Workshop on Multimodal Language ACL 202
Multimedia search without visual analysis: the value of linguistic and contextual information
This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features
From media crossing to media mining
This paper reviews how the concept of Media Crossing has contributed to the advancement of the application domain of information access and explores directions for a future research agenda. These will include themes that could help to broaden the scope and to incorporate the concept of medium-crossing in a more general approach that not only uses combinations of medium-specific processing, but that also exploits more abstract medium-independent representations, partly based on the foundational work on statistical language models for information retrieval. Three examples of successful applications of media crossing will be presented, with a focus on the aspects that could be considered a first step towards a generalized form of media mining
Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos
This paper presents a novel approach to perform sentiment analysis of news
videos, based on the fusion of audio, textual and visual clues extracted from
their contents. The proposed approach aims at contributing to the
semiodiscoursive study regarding the construction of the ethos (identity) of
this media universe, which has become a central part of the modern-day lives of
millions of people. To achieve this goal, we apply state-of-the-art
computational methods for (1) automatic emotion recognition from facial
expressions, (2) extraction of modulations in the participants' speeches and
(3) sentiment analysis from the closed caption associated to the videos of
interest. More specifically, we compute features, such as, visual intensities
of recognized emotions, field sizes of participants, voicing probability, sound
loudness, speech fundamental frequencies and the sentiment scores (polarities)
from text sentences in the closed caption. Experimental results with a dataset
containing 520 annotated news videos from three Brazilian and one American
popular TV newscasts show that our approach achieves an accuracy of up to 84%
in the sentiments (tension levels) classification task, thus demonstrating its
high potential to be used by media analysts in several applications,
especially, in the journalistic domain.Comment: 5 pages, 1 figure, International AAAI Conference on Web and Social
Medi
Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies
Stories can have tremendous power -- not only useful for entertainment, they
can activate our interests and mobilize our actions. The degree to which a
story resonates with its audience may be in part reflected in the emotional
journey it takes the audience upon. In this paper, we use machine learning
methods to construct emotional arcs in movies, calculate families of arcs, and
demonstrate the ability for certain arcs to predict audience engagement. The
system is applied to Hollywood films and high quality shorts found on the web.
We begin by using deep convolutional neural networks for audio and visual
sentiment analysis. These models are trained on both new and existing
large-scale datasets, after which they can be used to compute separate audio
and visual emotional arcs. We then crowdsource annotations for 30-second video
clips extracted from highs and lows in the arcs in order to assess the
micro-level precision of the system, with precision measured in terms of
agreement in polarity between the system's predictions and annotators' ratings.
These annotations are also used to combine the audio and visual predictions.
Next, we look at macro-level characterizations of movies by investigating
whether there exist `universal shapes' of emotional arcs. In particular, we
develop a clustering approach to discover distinct classes of emotional arcs.
Finally, we show on a sample corpus of short web videos that certain emotional
arcs are statistically significant predictors of the number of comments a video
receives. These results suggest that the emotional arcs learned by our approach
successfully represent macroscopic aspects of a video story that drive audience
engagement. Such machine understanding could be used to predict audience
reactions to video stories, ultimately improving our ability as storytellers to
communicate with each other.Comment: Data Mining (ICDM), 2017 IEEE 17th International Conference o
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