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2005, Journal of The Brazilian Computer Society
Searching for information in long videos can be a time-consuming experience. In this paper, we describe OnAIR, an ontology-aided information retrieval system applied to retrieve clips from video collections. We used a video collection compiled from interviews with Ana Teixeira, a Brazilian artist. The interviews were made by Paula P. Braga, the domain expert. The interview is developed in the domain of contemporary art and the system uses a domain ontology to expand the queries with related terms. We tested the system with a battery of queries, and we veri.ed that the ontology contributes to the e.ciency improvement in terms of the relevance of retrieved documents. We designed the system to work in a domain-independent way, allowing us to move to other domains by just changing the underlying ontologies and video collections.
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.
2007
We outline DLMedia, an ontology mediated multimedia information retrieval system, which combines logic-based retrieval with multimedia featurebased similarity retrieval. An ontology layer may be used to define (in terms of a DLR-Lite like description logic) the relevant abstract concepts and relations of the application domain, while a content-based multimedia retrieval system is used for feature-based retrieval.
2017
Recent era there is an increase in the use of videobased applications which revealed the need for extracting the content in videos and how semantically they are interrelated. In this paper we propose a semantic content extraction system that allows users to query and retrieve objects, events, and concepts that are extracted automatically by building the ontology with respect to events and related interest. This paper discusses retrieval techniques of multimedia content using ontology semantically.
Lecture Notes in Computer Science, 2004
Domain ontologies are very useful for indexing, query specification, retrieval and filtering, user interfaces, even information extraction from audiovisual material. The dominant emerging language standard for the description of domain ontologies is OWL. We describe here a methodology and software that we have developed for the interoperability of OWL with the complete MPEG-7 MDS so that domain ontologies described in OWL can be transparently integrated with the MPEG-7 MDS metadata. This allows applications that recognize and use the MPEG-7 MDS constructs to make use of domain ontologies for applications like indexing, retrieval, filtering etc. resulting in more effective user retrieval and interaction with audiovisual material.
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...
2012
This paper introduces the reader to the approach we are taking to develop an ontology that could be used to represent the knowledge inherent in filmed materials. Such an ontology could be used as the semantic basis for multimedia retrieval systems. The proposed approach to ontology development is informed by the earlier work of researchers into folksonomy development and facet analysis. A brief survey of this earlier work is presented before our approach is described.
2011
Multimedia plays significant role in today’s IT world. The revolution of multimedia makes it more familiar to the users because of its expressiveness. Multimedia has a wide range of application in many fields like e-learning, teleconferencing, online medical transcription, etc. Semantic web is an emerging technology to fulfil the user’s needs. Over the past decade, lot of research are going on for retrieval of multimedia content for semantic web. This paper discusses various retrieval techniques of multimedia content using ontology for the semantic web. In addition, it discusses the advantages of Text, Image, Video and Audio based retrieval systems. It also presents an analysis based on
ijetrm journal
Today, Information Technology (IT) is rapidly improving. Large-scale available data and information are demanded by information society. To organize large information repositories and access these repositories efficiently, metadata can be used. Metadata is closely related to Ontology. Ontology is a formal explicit description of concepts in a domain of discourse, properties of each concept describing various features and attributes of the concept, and restrictions on slots. This work intends to realize how to create an ontology for image annotation and retrieval system. In this paper, we simulate a search engine based on the ontology concept for retrieving the desired image and related information. As a case study, the system uses the student information from University of Computer Studies (Mandalay). The retrieval system is implemented by using Java, and Java API for developing Resource Description Framework (RDF) files. These RDF files can give the image and related information for the user"s requirement. INTRODUCTION Information is crucial in the process of planning and making decisions. Many researchers concentrating on retrieval system to automate the process of selecting, filtering and searching the desired information. In this way, much of the information will be reduced and the result will consist of higher information density more compact and valuable information. Information Retrieval (IR) deals with the representation, storage, organization and access to information items. The representation and organization of the information items should provide the user with easy access to the information in which user is interested. The purpose of the IR system is to capture the desired items and to filter out unwanted items. To build the large information repositories and access these repositories efficiently, metadata can be used. Metadata is closely related to Ontology. The idea of ontologies is that they are conceptually motivated, i.e., can be used to express the intended meaning of things, and not just words as textual strings. In [1] described how ontologies can be used to create better image annotation and retrieval systems. In a nutshell, ontologies are used to overcome the problems that evolve from traditional text-based information retrieval when it is applied to images. [2] discussed an ontology-based image retrieval approach that aims to standardize image description and the understanding of semantic content. Ontology-based image retrieval has the potential to fully describe the semantic content of an image, allowing the similarity between images and retrieval query to be computed accurately. This work is to design and develop an architecture that can retrieve desired image and related information from the student ontology. The system uses the information from first year to fourth year students in University of Computer Studies (Mandalay). The organization of remaining sections is as follows: Section II describes the basic aspect of ontology. In section III, we devote the design process to build the ontology-based retrieval system. Preliminary testing is carried out on the system, which is explored in section IV. Finally, we conclude the paper in section V.
Library Hi Tech, 2014
Purpose – The overwhelming speed and scale of digital media production greatly outpace conventional indexing methods by humans. The management of Big Data for e-library speech resources requires an automated metadata solution. The paper aims to discuss these issues. Design/methodology/approach – A conceptual model called semantic ontologies for multimedia indexing (SOMI) allows for assembly of the speech objects, encapsulation of semantic associations between phonic units and the definition of indexing techniques designed to invoke and maximize the semantic ontologies for indexing. A literature review and architectural overview are followed by evaluation techniques and a conclusion. Findings – This approach is only possible because of recent innovations in automated speech recognition. The introduction of semantic keyword spotting allows for indexing models that disambiguate and prioritize meaning using probability algorithms within a word confusion network. By the use of AI error-t...
Procedia Computer Science, 2016
Numerous educational video lectures, CCTV surveillance, transport and other types have upgraded the impact of multimedia video content. In order to make large video databases realistic, video data has to be automatically indexed in order to search and retrieve relevant material. An annotation is a markup reference made to data in video in order to improve the video accessibility. Video annotation is used to examine the massive quantity of multimedia data in the repositories. Video annotation refers to the taking out of significant data present in video and placing this data to the video can benefit in "retrieval, browsing, analysis, searching comparison and categorization". Video annotation implies taking out of data and to attach such metadata to the video which will "accelerate the retrieval speed, ease of access, analysis and categorization". It permits fast and better understanding of video content and improves the performance of retrieval and decreases human time & efforts for better study of videos. Video annotation is imperative technique that assists in video access. Proposed system provides effortless access to the data of the video and decrease the time necessary to access and evaluate the video. Ontology-based video annotation helps the user to get the semantic information from video, which is essential to search the needful data from a video.
Proceedings of the 13th annual ACM international conference on Multimedia - MULTIMEDIA '05, 2005
To ensure access to growing video collections, annotation is becoming more and more important. Using background knowledge in the form of ontologies or thesauri is a way to facilitate annotation in a broad domain. Current ontologies are not suitable for (semi-) automatic annotation of visual resources as they contain little visual information about the concepts they describe. We investigate how an ontology that does contain visual information can facilitate annotation in a broad domain and identify requirements that a visual ontology has to meet. Based on these requirements, we create a visual ontology out of two existing knowledge corpora (WordNet and MPEG-7) by creating links between visual and general concepts. We test performance of the ontology on 40 shots of news video, and discuss the added value of each visual property.
2007
The development of appropriate tools and solutions to support effective access to video content is one of the main challenges for video digital libraries. Different techniques for manual and automatic annotation and retrieval have been proposed in recent years. It is a common practice to use linguistic ontologies for video annotation and retrieval: video elements are classified by establishing relationships between video contents and linguistic terms that identify domain concepts at different abstraction levels. However, although linguistic terms are appropriate to distinguish event and object categories, they are inadequate when they must describe specific or complex patterns of events or video entities. Instead, in these cases, pattern specifications can be better expressed using visual prototypes, either images or video clips, that capture the essence of the event or entity. High level concepts, expressed trough linguistic terms, and patterns specification, represented by visual prototypes, can be both organized into new extended ontologies where images or video clips are added to the ontologies as specification of linguistic terms. This paper presents algorithms and techniques that employ enriched ontologies for video annotation and retrieval, and discusses a solution for their implementation for the soccer video domain. An unsupervised clustering method is proposed in order to create multimedia enriched ontologies by defining visual prototypes that represent specific patterns of highlights and adding them as visual concepts to the ontology. An algorithm that uses multimedia enriched ontologies to perform automatic soccer video annotation is proposed and results for typical highlights are presented. Annotation is performed associating occurrences of events, or entities, to higher level concepts by checking their similarity to visual concepts that are hierarchically linked to higher level semantics, using a dynamic programming approach. Usage of reasoning on the ontology is shown, to create complex queries that comprise visual prototypes of actions, their temporal evolution and relations.
2020
The diversity of Southeast Asian Intangible Cultural Heritage (ICH) is showcased in many art forms and notably in traditional dances. We focus on the preservation of Vietnamese ICH by building an ontology for Tamia đwa buk dances. We propose a completion of the ontology by semantically enriching traditional dance videos through manual annotation. Once annotated video datasets are built, we propose strategies for processing user queries. In particular, we address inconsistencies which emerge when the same video receives conflicting annotations from multiple sources. We also take into account different reliability levels of the sources in order to prioritize query answers.
Lecture Notes in Computer Science, 2007
Ontologies are defined as the representation of the semantics of terms and their relationships. Traditionally, they consist of concepts, concept properties, and relationships between concepts, all expressed in linguistic terms. In order to support effectively video annotation and content-based retrieval the traditional linguistic ontologies should be extended to include structural video information and perceptual elements such as visual data descriptors.
International Journal of Multimedia Information Retrieval, 2012
A domain-specific ontology models a specific domain or part of the world. In fact, ontologies have proven to be an excellent medium for capturingpagebreak the knowledge of a domain. We propose an ontology learning scheme in this paper which combines standard multimedia analysis techniques with knowledge drawn from conceptual metadata to learn a domain-specific multimedia ontology from a set of annotated examples. A standard machine-learning algorithm that learns structure and parameters of a Bayesian network is extended to include media observables in the learning. An expert group provides domain knowledge to construct a basic ontology of the domain as well as to annotate a set of training videos. These annotations help derive the associations between high-level semantic concepts of the domain and low-level media features. We construct a more robust and refined version of the basic ontology by learning from this set of conceptually annotated data. We show an application of our ontology-based framework for exploration of multimedia content, in the field of cultural heritage preservation. By constructing an ontology for the cultural heritage domain of Indian classical dance, and by offering an application for semantic annotation of the heritage collection of Indian dance videos, we demonstrate the efficacy of ou approach.
2007
Nowadays, an increasingly growing demand for the creation of digital multimedia libraries is arising, as huge amounts of digital visual content are becoming available. The content that resides in these libraries should be easily retrievable and classified in order to be fully accessible.
2007
The contribution of this paper is the introduction of a hybrid multimedia retrieval model accompanied by the presentation of a search engine that is capable of retrieving visual content from cultural heritage multimedia libraries as in three modes: (i) based on their semantic annotation with the help of an ontology; (ii) based on the visual features with a view to finding similar content; and (iii) based on the combination of these two strategies in order to produce recommendations. The main novelty is the way in which these two co-operate transparently during the evaluation of a single query in a hybrid fashion, making recommendations to the user and retrieving content that is both visually and semantically similar.
2003
The binary form of an image does not tell what the image is about. It is possible to retrieve images from a database using pattern matching techniques, but usually textual descriptions attached to the images are used. Semantic web ontology and metadata languages provide a new way to annotating and retrieving images. This paper considers the situation when a user is faced with an image repository whose content is complicated and semantically unknown to some extent. We show how ontologies can then be of help to the user in formulating the information need, the query, and the answers. As a proof of the concept, we have implemented a demonstrational photo exhibition using the promotion image database of the Helsinki University Museum based on semantic web technologies. In this system, images are annotated according to ontologies and the same conceptualization is offered to the user to facilitate focused image retrieval using the right terminology. When generating answers to the queries, the ontology combined with the image data also facilitates, e.g., recommendation of semantically related images to the user.
2006
Abstract Effective usage of multimedia digital libraries has to deal with the problem of building efficient content annotation and retrieval tools. MOM (Multimedia Ontology Manager) is a complete system that allows the creation of multimedia ontologies, supports automatic annotation and creation of extended text (and audio) commentaries of video sequences, and permits complex queries by reasoning on the ontology.
IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing - Vol 2 - Workshops, 2006
Recent advances in computer power, network bandwidth, information storage, and signal processing techniques have led to a proliferation of multimedia data. As a result, multimedia has become one of the most important information sources. To ensure effective utilization of multimedia assets by a variety of users, we propose a multi-ontology based multimedia annotation model, in which domain independent multimedia ontology is integrated with multiple domain ontologies in an effort to provide multiple, domain-specific views of multimedia content such that multimedia access can better address different users' information needs. We have developed a Multimedia Ontology(MO) based on MPEG-7 multimedia content description tools, proposed a strategy to integrate multiple domain ontologies, and designed a term extraction procedure to automatically extract domain-specific ontological terms from textual resources of multimedia data. To evaluate multiontology based multimedia annotation, we have developed a multi-ontology based multimedia access platform with a web-based interface. The preliminary evaluation suggests that multi-ontology based multimedia annotation can better meet different users' information needs.
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