This media streaming architecture solves the problems of collaborative session management, from session sharing to control of the common session state to session transfer among networked clients.
In this thesis we present a complete image retrieval system based on topic models and evaluate the suitability of different types of topic models for the task of large-scale retrieval on realworld databases. Different similarity measure are evaluated in a retrieval-by-example task.Next, we focus on the incorporation of different types of local image features in the topic models. For this, we first evaluate which types of feature detectors and descriptors are appropriate to model the images, then we propose and explore models that fuse multiple types of local features. All basic topic models require the quantization of the otherwise high-dimensional continuous local feature vectors into a finite, discrete vocabulary to enable the bag-of-words image representation the topic models are built on. As it is not clear how to optimally quantize the high-dimensional features, we introduce different extensions to a basic topic model which model the visual vocabulary continuously, making the quantization step obsolete.On-line image repositories of the Web 2.0 often store additional information about the images besides their pixel values, called metadata, such as associated tags, date of creation, ownership and camera parameters. In this work we also investigate how to include such cues in our retrieval system. We present work in progress on (hierarchical) models which fuse features from multiple modalities.Finally, we present an approach to find the most relevant images, i.e., very representative images, in a large web-scale collection given a query term. Our unsupervised approach ranks highest the image whose image content and its various metadata types gives us the highest probability according to a the model we automatically build for this tag.
Digital video streaming has attracted large interest in research as well as in commercial areas in recent years. The evolution of digital video coding and broadband Internet access enables a large number of users to access high quality video streams with several devices varying from mobile phones to notebooks. However, digital video streaming still has high resource requirements concerning the transmission and decoding of the streams. Especially mobile devices often cannot comply with such resource demands. This paper briefly describes our multimedia gateway implementation which provides video adaptation to mobile clients by using multidimensional compressed domain transcoding mechanisms.
Abstract-The number and types of mobile devices which are capable of presenting digital video streams is increasing constantly. In most cases the devices are trade-offs between powerful all-purpose computers and small mobile devices which are ubiquitously available and range from cellular phones to notebooks. This great heterogeneity of mobile devices makes video streaming to such devices a challenging task for content providers. Each single device has its own capabilities and individual requirements, which need to be considered when sending a video stream to it. Thus, to support a great range of different devices, the video streams need to be adapted to the requirements of each device. To get an idea of how different adaptation methods may affect the experience of users watching a streamed video on a mobile device, we inspect the influence of three major adaptation dimensions on the produced quality of the stream. Based on these results, we are able to give a clear recommendation for a multidimensional adaptation of digital video streams.
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