9,932 research outputs found
Video semantic content analysis framework based on ontology combined MPEG-7
The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. New multimedia standard, MPEG-7, provides the rich functionalities to enable the generation of audiovisual descriptions and is expressed solely in XML Schema which provides little support for expressing semantic knowledge. In this paper, a video semantic content analysis framework based on ontology combined MPEG-7 is presented. Domain
ontology is used to define high level semantic concepts and their relations in the context of the examined domain. MPEG-7 metadata terms of audiovisual descriptions and video content analysis algorithms are expressed in this ontology to enrich video semantic analysis. OWL is used for the ontology description. Rules in Description Logic are defined to describe how low-level features and algorithms for video analysis should be applied according to different perception content. Temporal Description Logic is used to describe the
semantic events, and a reasoning algorithm is proposed for events detection. The proposed framework is demonstrated in sports video domain and shows promising results
Video semantic content analysis based on ontology
The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. New multimedia standards, such as MPEG-4 and MPEG-7, provide the basic functionalities in order to manipulate and transmit objects and metadata. But importantly, most of the content of video data at a semantic level is out of the scope of the standards. In this paper, a video semantic content analysis framework based on ontology is presented. Domain ontology is used to define high level semantic concepts and their relations in the context of the examined domain. And low-level features (e.g. visual and aural) and video content analysis algorithms are integrated into the ontology to enrich video semantic analysis. OWL is used for the ontology description. Rules in Description Logic are defined to describe how features and algorithms for video analysis should be applied according to different perception content and low-level features. Temporal Description Logic is used to describe the semantic events, and a reasoning algorithm is proposed for events detection. The proposed framework is demonstrated in a soccer video domain and shows promising results
Automatic Synchronization of Multi-User Photo Galleries
In this paper we address the issue of photo galleries synchronization, where
pictures related to the same event are collected by different users. Existing
solutions to address the problem are usually based on unrealistic assumptions,
like time consistency across photo galleries, and often heavily rely on
heuristics, limiting therefore the applicability to real-world scenarios. We
propose a solution that achieves better generalization performance for the
synchronization task compared to the available literature. The method is
characterized by three stages: at first, deep convolutional neural network
features are used to assess the visual similarity among the photos; then, pairs
of similar photos are detected across different galleries and used to construct
a graph; eventually, a probabilistic graphical model is used to estimate the
temporal offset of each pair of galleries, by traversing the minimum spanning
tree extracted from this graph. The experimental evaluation is conducted on
four publicly available datasets covering different types of events,
demonstrating the strength of our proposed method. A thorough discussion of the
obtained results is provided for a critical assessment of the quality in
synchronization.Comment: ACCEPTED to IEEE Transactions on Multimedi
Analysis and Forecasting of Trending Topics in Online Media Streams
Among the vast information available on the web, social media streams capture
what people currently pay attention to and how they feel about certain topics.
Awareness of such trending topics plays a crucial role in multimedia systems
such as trend aware recommendation and automatic vocabulary selection for video
concept detection systems.
Correctly utilizing trending topics requires a better understanding of their
various characteristics in different social media streams. To this end, we
present the first comprehensive study across three major online and social
media streams, Twitter, Google, and Wikipedia, covering thousands of trending
topics during an observation period of an entire year. Our results indicate
that depending on one's requirements one does not necessarily have to turn to
Twitter for information about current events and that some media streams
strongly emphasize content of specific categories. As our second key
contribution, we further present a novel approach for the challenging task of
forecasting the life cycle of trending topics in the very moment they emerge.
Our fully automated approach is based on a nearest neighbor forecasting
technique exploiting our assumption that semantically similar topics exhibit
similar behavior.
We demonstrate on a large-scale dataset of Wikipedia page view statistics
that forecasts by the proposed approach are about 9-48k views closer to the
actual viewing statistics compared to baseline methods and achieve a mean
average percentage error of 45-19% for time periods of up to 14 days.Comment: ACM Multimedia 201
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