Sirio

2010

Abstract In this technical demonstration we show an integrated web system for video search and annotation based on ontologies. The system is composed by three components: the Orione ontology-based search engine, the Sirio\ footnote {Sirio was the hound of Orione. It was a dog so swift that no prey could escape it.} search interface, and the Pan web-based video annotation tool. The system is currently being developed within the EU IM3I project.

International Journal of Digital Culture and Electronic Tourism, 2009

The paper presents an ontological approach for enabling semantic-aware information retrieval and browsing framework facilitating the user access to its preferred contents. Through the ontologies the system will express key entities and relationships describing learning material in a formal machineprocessable representation. An ontology-based knowledge representation could be used for content analysis and concept recognition, for reasoning processes and for enabling user-friendly and intelligent multimedia content search and retrieval.

2011

Video is becoming vital to society and economy. It plays a key role in information distribution and access, and it is also becoming the natural form of communication on the Internet and via mobile devices. The massive increase in digital audiovisual information will pose high demands on advanced storage and retrieval engines, and it is certain that consumers and professionals will need advanced storage and search technologies for the management of large-scale video assets.

2010

With the exponential growth of video data on the World Wide Web comes the challenge of efficient methods in video content management, content-based video search, filtering and browsing. But, video data often lacks sufficient metadata to open up the video content and to enable pinpoint content-based search. With the advent of the 'web of data' as an extension of the current WWW new data sources can be exploited by semantically interconnecting video metadata with the web of data. Thus, enabling better access to video repositories by deploying semantic search technologies and improving the user's search experience by supporting exploratory search strategies. We have developed the prototype semantic video search engine 'yovisto' that demonstrates the advantages of semantically enhanced exploratory video search and enables investigative navigation and browsing in large video repositories.

… Indexing (CBMI), 2011 …, 2011

In this paper we present two interactive systems for video search and browsing; one is a web application based on the Rich Internet Application paradigm, designed to obtain the levels of responsiveness and interactivity typical of a desktop application, while the other exploits multi-touch devices to implement a multi-user collaborative application. Both systems use the same ontology-based video search engine, that is capable of expanding user queries through ontology reasoning, and let users to search for specific video segments that contain a semantic concept or to browse the content of video collections, when it's too difficult to express a specific query.

Nowadays, the video documents like educational courses available on the web increases significantly. However, the information retrieval systems today can not return to the users (students or teachers) of parts of those videos that meet their exact needs expressed by a query consisting of semantic information. In this paper, we present a model of pedagogical knowledge of current videos. This knowledge is used throughout the process of indexing and semantic search segments instructional videos. Our experimental results show that the proposed approach is promising.

2010

Abstract In this technical demonstration we present a novel web-based tool that allows a user friendly semantic browsing of video collections, based on ontologies, concepts, concept relations and concept clouds. The system is developed as a Rich Internet Application (RIA) to achieve a fast responsiveness and ease of use that can not be obtained by other web application paradigms, and uses streaming to access and inspect the videos.

International Journal of Multimedia Data Engineering and Management, 2011

This paper examines video retrieval based on Query-By-Example (QBE) approach, where shots relevant to a query are retrieved from large-scale video data based on their similarity to example shots. This involves two crucial problems: The first is that similarity in features does not necessarily imply similarity in semantic content. The second problem is an expensive computational cost to compute the similarity of a huge number of shots to example shots. The authors have developed a method that can filter a large number of shots irrelevant to a query, based on a video ontology that is knowledge base about concepts displayed in a shot. The method utilizes various concept relationships (e.g., generalization/specialization, sibling, part-of, and co-occurrence) defined in the video ontology. In addition, although the video ontology assumes that shots are accurately annotated with concepts, accurate annotation is difficult due to the diversity of forms and appearances of the concepts. Demps...

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2007

In this paper we present the methods underlying the Medi-aMill semantic video search engine. The basis for the engine is a semantic indexing process which is currently based on a lexicon of 491 concept detectors. To support the user in navigating the collection, the system defines a visual similarity space, a semantic similarity space, a semantic thread space, and browsers to explore them. We compare the different browsers and their utility within the TRECVID benchmark. In 2005, We obtained a top-3 result for 19 out of 24 search topics. In 2006 for 14 out of 24.

2009 IEEE International Conference on Semantic Computing, 2009

This paper aims to provide a semantic web based video search engine. Currently, we do not have scalable integration platforms to represent extracted features from videos, so that they could be indexed and searched. The task of indexing extracted features from videos is a difficult challenge, due to the diverse nature of the features and the temporal dimensions of videos. We present a semantic web based framework for automatic feature extraction, storage, indexing and retrieval of videos. Videos are represented as interconnected set of semantic resources. Also, we suggest a new ranking algorithm for finding related resources which could be used in a semantic web based search engine.