.Implicitacquisitionofuserpreferencesmakeslog-basedcollaborative¿lteringfavorableinpracticetoaccomplishrecommendations.Inthispaper,wefollowaformalapproachintextretrievaltore-formulatetheproblem.Basedontheclassicprobabilityrankingprinciple,weproposeaprobabilisticuser-itemrelevancemodel.Underthisformalmodel,weshowthatuser-basedanditem-basedapproachesareonlytwodi¿erentfactorizationswithdi¿erentindependenceassumptions.Moreover,weshowthatsmoothingisanimportantaspecttoestimatetheparametersofthemodelsduetodatasparsity.Byaddinglinearinterpolationsmoothing,theproposedmodelgivesaprobabilisticjusti¿cationofusingTF×IDF-likeitemrankingincollaborative¿ltering.Besidesgivingtheinsightunderstandingoftheproblemofcollaborative¿ltering,wealsoshowexperimentsinwhichtheproposedmethodprovidesabetterrecommendationperformanceonamusicplay-listdataset.@en

Multimedia systems must deal with multiple data streams. Each data stream usually contains significant volume of redundant noisy data. In many real-time applications, it is essential to focus the computing resources on a relevant subset of data streams at any given time instant and use it to build the model of the environment. We formulate this problem as an experiential sampling problem and propose an approach to utilize computing resources efficiently on the most informative subset of data streams. First, in this paper, we focus on theoretical background and develop a theoretical framework for a single data stream. We generalize the notion of static visual attention in a dynamical systems setting and propose a dynamical attention-orientated analysis method. This is achieved by a sampling representation that utilizes the current context and past experience for attention evolution. Hence, the multimedia analysis task at hand can select its data of interest while immediately discarding the irrelevant data to achieve efficiency and adaptability.@en

Abstract Most commercial television channels use video logos, which can be considered a form of visible watermark, as a declaration of intellectual property ownership. They are also used as a symbol of authorization to rebroadcast when original logos are used in conjunction with newer logos. An unfortunate side effect of such logos is the concomitant decrease in viewing pleasure. In this paper, we use the temporal correlation of video frames to detect and remove video logos. In the video-logo-detection part, as an initial step, the logo boundary box is first located by using a distance threshold of video frames and is further refined by employing a comparison of edge lengths. Second, our proposed Bayesian classifier framework locates fragments of logos called logo-lets. In this framework, we systematically integrate the prior knowledge about the location of the video logos and their intrinsic local features to achieve a robust detection result. In our logo-removal part, after the logo region is marked, a matching technique is used to find the best replacement patch for the marked region within that video shot. This technique is found to be useful for small logos. Furthermore, we extend the image inpainting technique to videos. Unlike the use of 2D gradients in the image inpainting technique, we inpaint the logo region of video frames by using 3D gradients exploiting the temporal correlations in video. The advantage of this algorithm is that the inpainted regions are consistent with the surrounding texture and hence the result is perceptually pleasing. We present the results of our implementation and demonstrate the utility of our method for logo removal. Keywords Video logo detection - Video logo removal - Video inpainting - Visual watermark attack - Neural network@en