6,832 research outputs found

    Unified Concept-based Multimedia Information Retrieval Technique

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    The explosion of digital data in the last two decades followed by the development of various types of data, including text, images, audio and video known as multimedia data. Multimedia Information Retrieval is required to search various type of media. There is comprehensive information need that can not be handled by the monolithic search engine like Google, Google Image, Youtube, or FindSounds. The shortcoming of search engine today related to their format or media is the dominance of text format, while the expected information could be an image, audio or video. Hence it is necessary to present multimedia format at the same time. This paper tries to design Unified Concept-based Multimedia Information Retrieval (UCpBMIR) technique to tackle those difficulties by using unified multimedia indexing. The indexing technique transforms the various of media with their features into text representation with the concept-based algorithm and put it into the concept detector. Learning model configures the concept detector to classify the multimedia object. The result of the concept detector process is placed in unified multimedia index database and waiting for the concept-based query to be matched into the Semantic Similarities with ontology. The ontology will provide the relationship between object representation of multimedia data. Due to indexing text, image, audio, and video respectively that naturally, they are heterogeneous, but conceptually they may have the relationship among them. From the preliminary result that multimedia document retrieved can be obtained through single query any format in order to retrieve all kind of multimedia format. Unified multimedia indexing technique with ontology will unify each format of multimedia

    Unified Concept-based Multimedia Information Retrieval Technique

    Get PDF
    The explosion of digital data in the last two decades followed by the development of various types of data, including text, images, audio and video known as multimedia data. Multimedia Information Retrieval is required to search various type of media. There is comprehensive information need that can not be handled by the monolithic search engine like Google, Google Image, Youtube, or FindSounds. The shortcoming of search engine today related to their format or media is the dominance of text format, while the expected information could be an image, audio or video. Hence it is necessary to present multimedia format at the same time. This paper tries to design Unified Concept-based Multimedia Information Retrieval (UCpBMIR) technique to tackle those difficulties by using unified multimedia indexing. The indexing technique transforms the various of media with their features into text representation with the concept-based algorithm and put it into the concept detector. Learning model configures the concept detector to classify the multimedia object. The result of the concept detector process is placed in unified multimedia index database and waiting for the concept-based query to be matched into the Semantic Similarities with ontology. The ontology will provide the relationship between object representation of multimedia data. Due to indexing text, image, audio, and video respectively that naturally, they are heterogeneous, but conceptually they may have the relationship among them. From the preliminary result that multimedia document retrieved can be obtained through single query any format in order to retrieve all kind of multimedia format. Unified multimedia indexing technique with ontology will unify each format of multimedi

    Measuring concept similarities in multimedia ontologies: analysis and evaluations

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    The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure of a multimedia ontology and existing inter-concept relations has not received similar attention. In this paper, we present a clustering-based method for modeling semantic concepts on low-level feature spaces and study the evaluation of the quality of such models with entropy-based methods. We cover a variety of methods for assessing the similarity of different concepts in a multimedia ontology. We study three ontologies and apply the proposed techniques in experiments involving the visual and semantic similarities, manual annotation of video, and concept detection. The results show that modeling inter-concept relations can provide a promising resource for many different application areas in semantic multimedia processing

    An empirical study of inter-concept similarities in multimedia ontologies

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    Generic concept detection has been a widely studied topic in recent research on multimedia analysis and retrieval, but the issue of how to exploit the structure of a multimedia ontology as well as different inter-concept relations, has not received similar attention. In this paper, we present results from our empirical analysis of different types of similarity among semantic concepts in two multimedia ontologies, LSCOM-Lite and CDVP-206. The results show promise that the proposed methods may be helpful in providing insight into the existing inter-concept relations within an ontology and selecting the most facilitating set of concepts and hierarchical relations. Such an analysis as this can be utilized in various tasks such as building more reliable concept detectors and designing large-scale ontologies

    Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up approaches

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    Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches

    Text-based Semantic Annotation Service for Multimedia Content in the Esperonto project

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    Within the Esperonto project, an integration of NLP, ontologies and other knowledge bases, is being performed with the goal to implement a semantic annotation service that upgrades the actual Web towards the emerging Semantic Web. Research is being currently conducted on how to apply the Esperonto semantic annotation service to text material associated with still images in web pages. In doing so, the project will allow for semantic querying of still images in the web, but also (automatically) create a large set of text-based semantic annotations of still images, which can be used by the Multimedia community in order to support the task of content indexing of image material, possibly combining the Esperonto type of annotations with the annotations resulting from image analysis

    Video semantic content analysis based on ontology

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    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
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