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Tag suggestion and localization in user-generated videos based on social knowledge
- In Proc. of ACM WSM
, 2010
"... Nowadays, almost any web site that provides means for sharinguser-generatedmultimediacontent, likeFlickr, Facebook, YouTube and Vimeo, has tagging functionalities to let users annotate the material that they want to share. The tags are then used to retrieve the uploaded content, and to ease browsing ..."
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Nowadays, almost any web site that provides means for sharinguser-generatedmultimediacontent, likeFlickr, Facebook, YouTube and Vimeo, has tagging functionalities to let users annotate the material that they want to share. The tags are then used to retrieve the uploaded content, and to ease browsing and exploration of these collections, e.g. using tag clouds. However, while tagging a single image is straightforward, and sites like Flickr and Facebook allow also to tag easily portions of the uploaded photos, tagging a video sequence is more cumbersome, so that users just tend to tag the overall content of a video. Moreover, the tagging process is completely manual, and often users tend to spend as few time as possible to annotate the material, resulting in a sparse annotation of the visual content. A semi-automatic process, that helps the users to tag a video sequence would improve the quality of annotations and thus the overall user experience. While research on image tagging has received a considerable attention in the latest years, there are still very few works that address the problem of automatically assigning tags to videos, locating them temporally within the video sequence. In this paper we present a system for video tag suggestion and temporal localization based on collective knowledge and visual similarity of frames. The algorithm suggests new tags that can be associated to a given keyframe exploiting the tags associated to videos and images uploaded to social sites like YouTube and Flickr and visual features. Categories andSubjectDescriptors
Crowdsourcing Event Detection in YouTube Videos
"... Abstract. Considerable efforts have been put into making video content on the Web more accessible, searchable, and navigable by research on both textual and visual analysis of the actual video content and the accompanying metadata. Nevertheless, most of the time, videos are opaque objects in website ..."
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Abstract. Considerable efforts have been put into making video content on the Web more accessible, searchable, and navigable by research on both textual and visual analysis of the actual video content and the accompanying metadata. Nevertheless, most of the time, videos are opaque objects in websites. With Web browsers gaining more support for the HTML5 <video> element, videos are becoming first class citizens on the Web. In this paper we show how events can be detected on-the-fly through crowdsourcing (i) textual, (ii) visual, and (iii) behavioral analysis in YouTube videos, at scale. The main contribution of this paper is a generic crowdsourcing framework for automatic and scalable semantic annotations of HTML5 videos. Eventually, we discuss our preliminary results using traditional server-based approaches to video event detection as a baseline. 1
Semantics in social tagging systems: A review
- Computer Networks and Information Technology (ICCNIT), International Conference (2011
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Personalised Graph-based Selection of Web APIs
- In Proceedings of the 11th International Semantic Web Conference (ISWC). Lecture Notes in Computer Science
, 2012
"... Abstract. Modelling and understanding various contexts of users is important to enable personalised selection of Web APIs in directories such as Programmable Web. Currently, relationships between users and Web APIs are not clearly understood and utilized by existing selection approaches. In this pa ..."
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Abstract. Modelling and understanding various contexts of users is important to enable personalised selection of Web APIs in directories such as Programmable Web. Currently, relationships between users and Web APIs are not clearly understood and utilized by existing selection approaches. In this paper, we present a semantic model of a Web API directory graph that captures relationships such as Web APIs, mashups, developers, and categories. We describe a novel configurable graph-based method for selection of Web APIs with personalised and temporal aspects. The method allows users to get more control over their preferences and recommended Web APIs while they can exploit information about their social links and preferences. We evaluate the method on a real-world dataset from ProgrammableWeb.com, and show that it provides more contextualised results than currently available popularitybased rankings.
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"... Capturing the semantics of individual viewpoints on social signals in interpersonal communication ..."
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Capturing the semantics of individual viewpoints on social signals in interpersonal communication
© MIR Labs, www.mirlabs.net/ijcisim/index.html Adding Meaning to Social Network Microposts via Multiple Named Entity Disambiguation APIs and Tracking Their Data Provenance
"... Abstract: Social networking sites such as Facebook or Twitter let their users create microposts directed to all, or a subset of their contacts. Users can respond to microposts, or in addition to that, also click a Like or ReTweet button to show their appreciation for a certain micropost. Adding sema ..."
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Abstract: Social networking sites such as Facebook or Twitter let their users create microposts directed to all, or a subset of their contacts. Users can respond to microposts, or in addition to that, also click a Like or ReTweet button to show their appreciation for a certain micropost. Adding semantic meaning in the sense of unambiguous intended ideas to such microposts can, for example, be achieved via Natural Language Processing (NLP) and named entity disambiguation. Therefore, we have implemented a mash-up NLP API, which is based on a combination of several third party NLP APIs in order to retrieve more accurate results in the sense of emergence. In consequence, our API uses third party APIs opaquely in the background to deliver its output. In this paper, we describe how one can keep track of data provenance and credit back the contributions of each single API to the joint result of the combined mash-up API. Therefore, we use the HTTP Vocabulary in RDF and the Provenance Vocabulary. In addition to that, we show how provenance metadata can help understand the way a combined result is formed, and optimize the result formation process.
Integrating and Interpreting Social Data from Heterogeneous Sources
"... Abstract. Social data is now being published at a never seen before scale. The provision of functionalities and features on a wide range of platforms from microblogging services to photo sharing platforms empowers users to generate content. However, such is the rate of publication, and the wide rang ..."
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Abstract. Social data is now being published at a never seen before scale. The provision of functionalities and features on a wide range of platforms from microblogging services to photo sharing platforms empowers users to generate content. However, such is the rate of publication, and the wide range of available platforms to facilitate the creation of social data, that interpreting this data is limited. In this paper we present an approach to interlink social data from multiple Social Web platforms by using Semantic Web technologies to achieve a consistent interpretation of the data. We present a web application to demonstrate the effectiveness of this approach, using the Cumbrian Floods in the UK as a use-case for anomaly detection within published social data. 1
