83,109 research outputs found

    Multi modal multi-semantic image retrieval

    Get PDF
    PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

    Towards the 3D Web with Open Simulator

    Get PDF
    Continuing advances and reduced costs in computational power, graphics processors and network bandwidth have led to 3D immersive multi-user virtual worlds becoming increasingly accessible while offering an improved and engaging Quality of Experience. At the same time the functionality of the World Wide Web continues to expand alongside the computing infrastructure it runs on and pages can now routinely accommodate many forms of interactive multimedia components as standard features - streaming video for example. Inevitably there is an emerging expectation that the Web will expand further to incorporate immersive 3D environments. This is exciting because humans are well adapted to operating in 3D environments and it is challenging because existing software and skill sets are focused around competencies in 2D Web applications. Open Simulator (OpenSim) is a freely available open source tool-kit that empowers users to create and deploy their own 3D environments in the same way that anyone can create and deploy a Web site. Its characteristics can be seen as a set of references as to how the 3D Web could be instantiated. This paper describes experiments carried out with OpenSim to better understand network and system issues, and presents experience in using OpenSim to develop and deliver applications for education and cultural heritage. Evaluation is based upon observations of these applications in use and measurements of systems both in the lab and in the wild.Postprin

    Designing an Open Virtual Factory of Small and Medium-sized Enterprises for Industrial Engineering Education

    Get PDF
    Curriculum of Industrial Engineering program must accomplish the requirement that graduates have the ability to design, develop, implement, and improve integrated system that include people, materials, equipment and energy. However, it is not easy to implement a curriculum that fosters such competencies. One of the strategies to achieve that is using an innovative learning media, so that the problem-based learning (PBL) can be accustomed. In this paper, we design a web-based enterprise resources planning. It is aimed to capture the real problem of small and medium-sized enterprises (SMEs) in bottled drinking water industries. The integrated system can be illustrated as ERP application that designed by using free open source software (FOSS). This research aimed to utilize the application to improve teaching methods in IE education. The result of the research can be used to improve the competencies of IE students, especially the abilities to identify, formulate, and solve the activities of the business process improvement in SMEs. Keywords Industrial engineering education, FOSS, innovative learning media, problem-based learnin
    corecore