423 research outputs found

    Automatic difference measure between movies using dissimilarity measure fusion and rank correlation coefficients

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    International audienceWhen considering multimedia database growth, one current challenging issue is to design accurate navigation tools. End user basic needs, such as exploration, similarity search and favorite suggestions, lead to investigate how to find semantically resembling media. One way is to build numerous continuous dissimilarity measures from low-level image features. In parallel, an other way is to build discrete dissimilarities from textual information which may be available with video sequences. However, how such different measures should be selected as relevant and be fused ? To this aim, the purpose of this paper is to compare all those various issimilarities and to propose a suitable ranking fusion method for several dissimilarities. Subjective tests with human observers on the CITIA animation movie database have been carried out to validate the model

    Deformable Groupwise Registration Using a Locally Low-Rank Dissimilarity Metric for Myocardial Strain Estimation from Cardiac Cine MRI Images

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    Objective: Cardiovascular magnetic resonance-feature tracking (CMR-FT) represents a group of methods for myocardial strain estimation from cardiac cine MRI images. Established CMR-FT methods are mainly based on optical flow or pairwise registration. However, these methods suffer from either inaccurate estimation of large motion or drift effect caused by accumulative tracking errors. In this work, we propose a deformable groupwise registration method using a locally low-rank (LLR) dissimilarity metric for CMR-FT. Methods: The proposed method (Groupwise-LLR) tracks the feature points by a groupwise registration-based two-step strategy. Unlike the globally low-rank (GLR) dissimilarity metric, the proposed LLR metric imposes low-rankness on local image patches rather than the whole image. We quantitatively compared Groupwise-LLR with the Farneback optical flow, a pairwise registration method, and a GLR-based groupwise registration method on simulated and in vivo datasets. Results: Results from the simulated dataset showed that Groupwise-LLR achieved more accurate tracking and strain estimation compared with the other methods. Results from the in vivo dataset showed that Groupwise-LLR achieved more accurate tracking and elimination of the drift effect in late-diastole. Inter-observer reproducibility of strain estimates was similar between all studied methods. Conclusion: The proposed method estimates myocardial strains more accurately due to the application of a groupwise registration-based tracking strategy and an LLR-based dissimilarity metric. Significance: The proposed CMR-FT method may facilitate more accurate estimation of myocardial strains, especially in diastole, for clinical assessments of cardiac dysfunction

    Collaborative Brain-Computer Interfaces in Rapid Image Presentation and Motion Pictures

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    The last few years have seen an increase in brain-computer interface (BCI) research for the able-bodied population. One of these new branches involves collaborative BCIs (cBCIs), in which information from several users is combined to improve the performance of a BCI system. This thesis is focused on cBCIs with the aim of increasing understanding of how they can be used to improve performance of single-user BCIs based on event-related potentials (ERPs). The objectives are: (1) to study and compare different methods of creating groups using exclusively electroencephalography (EEG) signals, (2) to develop a theoretical model to establish where the highest gains may be expected from creating groups, and (3) to analyse the information that can be extracted by merging signals from multiple users. For this, two scenarios involving real-world stimuli (images presented at high rates and movies) were studied. The first scenario consisted of a visual search task in which images were presented at high frequencies. Three modes of combining EEG recordings from different users were tested to improve the detection of different ERPs, namely the P300 (associated with the presence of events of interest) and the N2pc (associated with shifts of attention). We showed that the detection and localisation of targets can improve significantly when information from multiple viewers is combined. In the second scenario, feature movies were introduced to study variations in ERPs in response to cuts through cBCI techniques. A distinct, previously unreported, ERP appears in relation to such cuts, the amplitude of which is not modulated by visual effects such as the low-level properties of the frames surrounding the discontinuity. However, significant variations that depended on the movie were found. We hypothesise that these techniques can be used to build on the attentional theory of cinematic continuity by providing an extra source of information: the brain

    An Analytical Performance Evaluation on Multiview Clustering Approaches

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    The concept of machine learning encompasses a wide variety of different approaches, one of which is called clustering. The data points are grouped together in this approach to the problem. Using a clustering method, it is feasible, given a collection of data points, to classify each data point as belonging to a specific group. This can be done if the algorithm is given the collection of data points. In theory, data points that constitute the same group ought to have attributes and characteristics that are equivalent to one another, however data points that belong to other groups ought to have properties and characteristics that are very different from one another. The generation of multiview data is made possible by recent developments in information collecting technologies. The data were collected from Ă  variety of sources and were analysed using a variety of perspectives. The data in question are what are known as multiview data. On a single view, the conventional clustering algorithms are applied. In spite of this, real-world data are complicated and can be clustered in a variety of different ways, depending on how the data are interpreted. In practise, the real-world data are messy. In recent years, Multiview Clustering, often known as MVC, has garnered an increasing amount of attention due to its goal of utilising complimentary and consensus information derived from different points of view. On the other hand, the vast majority of the systems that are currently available only enable the single-clustering scenario, whereby only makes utilization of a single cluster to split the data. This is the case since there is only one cluster accessible. In light of this, it is absolutely necessary to carry out investigation on the multiview data format. The study work is centred on multiview clustering and how well it performs compared to these other strategies

    Combining Disparate Information for Machine Learning.

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    This thesis considers information fusion for four different types of machine learning problems: anomaly detection, information retrieval, collaborative filtering and structure learning for time series, and focuses on a common theme -- the benefit to combining disparate information resulting in improved algorithm performance. In this dissertation, several new algorithms and applications to real-world datasets are presented. In Chapter II, a novel approach called Pareto Depth Analysis (PDA) is proposed for combining different dissimilarity metrics for anomaly detection. PDA is applied to video-based anomaly detection of pedestrian trajectories. Following a similar idea, in Chapter III we propose to use a similar Pareto Front method for a multiple-query information retrieval problem when different queries represent different semantic concepts. Pareto Front information retrieval is applied to multiple query image retrieval. In Chapter IV, we extend a recently proposed collaborative retrieval approach to incorporate complementary social network information, an approach we call Social Collaborative Retrieval (SCR). SCR is applied to a music recommendation system that combines both user history and friendship network information to improve recall and weighted recall performance. In Chapter V, we propose a framework that combines time series data at different time scales and offsets for more accurate estimation of multiple precision matrices. We propose a general fused graphical lasso approach to jointly estimate these precision matrices. The framework is applied to modeling financial time series data.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108878/1/coolmark_1.pd
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