62 research outputs found

    Prosemantic features for content-based image retrieval

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    The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-18449-9_8Revised Selected Papers of 7th International Workshop, AMR 2009, Madrid, Spain, September 24-25, 2009We present here, an image description approach based on prosemantic features. The images are represented by a set of low-level features related to their structure and color distribution. Those descriptions are fed to a battery of image classifiers trained to evaluate the membership of the images with respect to a set of 14 overlapping classes. Prosemantic features are obtained by packing together the scores. To verify the effectiveness of the approach, we designed a target search experiment in which both low-level and prosemantic features are embedded into a content-based image retrieval system exploiting relevance feedback. The experiments show that the use of prosemantic features allows for a more successful and quick retrieval of the query images

    A framework for cloud-based context-aware information services for citizens in smart cities

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    © 2014 Khan et al.; licensee Springer. Background: In the context of smart cities, public participation and citizen science are key ingredients for informed and intelligent planning decisions and policy-making. However, citizens face a practical challenge in formulating coherent information sets from the large volumes of data available to them. These large data volumes materialise due to the increased utilisation of information and communication technologies in urban settings and local authorities’ reliance on such technologies to govern urban settlements efficiently. To encourage effective public participation in urban governance of smart cities, the public needs to be facilitated with the right contextual information about the characteristics and processes of their urban surroundings in order to contribute to the aspects of urban governance that affect them such as socio-economic activities, quality of life, citizens well-being etc. The cities on the other hand face challenges in terms of crowd sourcing with quality data collection and standardisation, services inter-operability, provisioning of computational and data storage infrastructure. Focus: In this paper, we highlight the issues that give rise to these multi-faceted challenges for citizens and public administrations of smart cities, identify the artefacts and stakeholders involved at both ends of the spectrum (data/service producers and consumers) and propose a conceptual framework to address these challenges. Based upon this conceptual framework, we present a Cloud-based architecture for context-aware citizen services for smart cities and discuss the components of the architecture through a common smart city scenario. A proof of concept implementation of the proposed architecture is also presented and evaluated. The results show the effectiveness of the cloud-based infrastructure for the development of a contextual service for citizens

    Semantic-based framework for integration and personalization of television related media

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    In this paper we try to identify requirements, opportunities and problems in home media centers and we propose an approach to address them by describing an intelligent home media environment. The major issues investigated are coping with the information overflow in the current provision of TV programs and channels and the need for personalization to specific users by adapting to their age, interests, language abilities, and various context characteristics. The research presented in this paper follows from a collaboration between Eindhoven University of Technology, the Philips Applied Technologies group and Stoneroos Interactive Television. The work has been partially carried out within the ITEA-funded European project Passepartout, which also includes partners like Thomson, INRIA and ETRI. In the following chapter we describe the motivation and research problem in relation to related work, followed by an illustrative use case scenario. Afterwards, we explain our data model which starts with explaining the TV-Anytime structure and its enrichments with semantic knowledge from various ontologies and vocabularies. The data model description then serves as the background for understanding our proposed system architecture SenSee. Afterwards we go deeper into the user modeling part and explain how our personalization approach works. The latter elaborates on a design targeting interoperability and on semantic techniques for enabling intelligent context-aware personalization. In the implementation chapter we describe some practical issues as well as our main interface showcase, iFanzy. Future work and conclusions end this chapter

    Survey papers in multimedia - Guest editorial

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    10.1007/s11042-010-0681-1Multimedia Tools and Applications5111-4MTAP

    Enhanced 1-D Chaotic Key-Based Algorithm for Image Encryption

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    A recently proposed Chaotic-Key Based Algorithm (CKBA) has been shown to be unavoidably susceptible to chosen/known-plaintext attacks and ciphertext-only attacks. In this paper we enhance the CKBA algorithm three-fold: 1) we change the 1-D chaotic Logistic map to a piecewise linear chaotic map (PWLCM) to improve the balance property, 2) we increase the key size to 128 bits, and 3) we add two more cryptographic primitives and extend the scheme to operate on multiple rounds so that the chosen/knownplaintext attacks are no longer possible. The new cipher has much stronger security and its performance characteristics remain very good

    Hot research topics - Guest editorial

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    10.1007/s11042-010-0682-0Multimedia Tools and Applications512397-400MTAP
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