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  • 1.
    Azizpour, Hossein
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Sharif Razavian, Ali
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Sullivan, Josephine
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Maki, Atsuto
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Carlssom, Stefan
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Factors of Transferability for a Generic ConvNet Representation2016In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 38, no 9, p. 1790-1802, article id 7328311Article in journal (Refereed)
    Abstract [en]

    Evidence is mounting that Convolutional Networks (ConvNets) are the most effective representation learning method for visual recognition tasks. In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units activation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a task with relatively smaller training set (target). Recent studies have shown this form of representation transfer to be suitable for a wide range of target visual recognition tasks. This paper introduces and investigates several factors affecting the transferability of such representations. It includes parameters for training of the source ConvNet such as its architecture, distribution of the training data, etc. and also the parameters of feature extraction such as layer of the trained ConvNet, dimensionality reduction, etc. Then, by optimizing these factors, we show that significant improvements can be achieved on various (17) visual recognition tasks. We further show that these visual recognition tasks can be categorically ordered based on their similarity to the source task such that a correlation between the performance of tasks and their similarity to the source task w.r.t. the proposed factors is observed.

  • 2.
    Björkman, Mårten
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Eklundh, Jan-Olof
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Real-time epipolar geometry estimation of binocular stereo heads2002In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 24, no 3, p. 425-432Article in journal (Refereed)
    Abstract [en]

    Stereo is an important cue for visually guided robots. While moving around in the world, such a robot can use dynamic fixation to overcome limitations in image resolution and field of view. In this paper, a binocular stereo system capable of dynamic fixation is presented. The external calibration is performed continuously taking temporal consistency into consideration, greatly simplifying the process. The essential matrix, which is estimated in real-time, is used to describe the epipolar geometry. It will be shown, how outliers can be identified and excluded from the calculations. An iterative approach based on a differential model of the optical flow, commonly used in structure from motion, is also presented and tested towards the essential matrix. The iterative method will be shown to be superior in terms of both computational speed and robustness, when the vergence angles are less than about 15degrees. For larger angles, the differential model is insufficient and the essential matrix is preferably used instead.

  • 3.
    Fukui, Kazuhiro
    et al.
    Tsukuba University, Japan.
    Maki, Atsuto
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Difference subspace and its generalization for subspace-based methods2015In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 37, no 11, p. 2164-2177Article in journal (Refereed)
    Abstract [en]

    Subspace-based methods are known to provide a practical solution for image set-based object recognition. Based on the insight that local shape differences between objects offer a sensitive cue for recognition, this paper addresses the problem of extracting a subspace representing the difference components between class subspaces generated from each set of object images independently of each other. We first introduce the difference subspace (DS), a novel geometric concept between two subspaces as an extension of a difference vector between two vectors, and describe its effectiveness in analyzing shape differences. We then generalize it to the generalized difference subspace (GDS) for multi-class subspaces, and show the benefit of applying this to subspace and mutual subspace methods, in terms of recognition capability. Furthermore, we extend these methods to kernel DS (KDS) and kernel GDS (KGDS) by a nonlinear kernel mapping to deal with cases involving larger changes in viewing direction. In summary, the contributions of this paper are as follows: 1) a DS/KDS between two class subspaces characterizes shape differences between the two respectively corresponding objects, 2) the projection of an input vector onto a DS/KDS realizes selective visualization of shape differences between objects, and 3) the projection of an input vector or subspace onto a GDS/KGDS is extremely effective at extracting differences between multiple subspaces, and therefore improves object recognition performance. We demonstrate validity through shape analysis on synthetic and real images of 3D objects as well as extensive comparison of performance on classification tests with several related methods; we study the performance in face image classification on the Yale face database B+ and the CMU Multi-PIE database, and hand shape classification of multi-view images.

  • 4.
    Henter, Gustav Eje
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. University of Edinburgh, United Kingdom.
    Kleijn, W. B.
    Minimum entropy rate simplification of stochastic processes2016In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. PP, no 99, article id 7416224Article in journal (Refereed)
    Abstract [en]

    We propose minimum entropy rate simplification (MERS), an information-theoretic, parameterization-independent framework for simplifying generative models of stochastic processes. Applications include improving model quality for sampling tasks by concentrating the probability mass on the most characteristic and accurately described behaviors while de-emphasizing the tails, and obtaining clean models from corrupted data (nonparametric denoising). This is the opposite of the smoothing step commonly applied to classification models. Drawing on rate-distortion theory, MERS seeks the minimum entropy-rate process under a constraint on the dissimilarity between the original and simplified processes. We particularly investigate the Kullback-Leibler divergence rate as a dissimilarity measure, where, compatible with our assumption that the starting model is disturbed or inaccurate, the simplification rather than the starting model is used for the reference distribution of the divergence. This leads to analytic solutions for stationary and ergodic Gaussian processes and Markov chains. The same formulas are also valid for maximum-entropy smoothing under the same divergence constraint. In experiments, MERS successfully simplifies and denoises models from audio, text, speech, and meteorology.

  • 5.
    Jalil, Taghia
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Arne, Leijon
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Variational Inference for Watson Mixture ModelIn: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539Article in journal (Other academic)
    Abstract [en]

    This paper addresses modelling data using the multivariate Watson distributions. The Watson distribution is one of thesimplest distributions for analyzing axially symmetric data. This distribution has gained some attention in recent years due to itsmodeling capability. However, its Bayesian inference is fairly understudied due to difficulty in handling the normalization factor. Recentdevelopment of Monte-Carlo Markov chain (MCMC) sampling methods can be applied for this purpose. However, these methods canbe prohibitively slow for practical applications. A deterministic alternative is provided by variational methods that convert inferenceproblems into optimization problems. In this paper, we present a variational inference for Watson mixture model. First, the variationalframework is used to side-step the intractability arising from the coupling of latent states and parameters. Second, the variational freeenergy is further lower bounded in order to avoid intractable moment computation. The proposed approach provides a lower bound onthe log marginal likelihood and retains distributional information over all parameters. Moreover, we show that it can regulate its owncomplexity by pruning unnecessary mixture components while avoiding over-fitting. We discuss potential applications of the modelingwith Watson distributions in the problem of blind source separation, and clustering gene expression data sets.

  • 6.
    Lindeberg, Tony
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Scale-space for discrete signals1990In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 12, no 3, p. 234-254Article in journal (Refereed)
    Abstract [en]

    This article addresses the formulation of a scale-space theory for discrete signals. In one dimension it is possible to characterize the smoothing transformations completely and an exhaustive treatment is given, answering the following two main questions:

    • Which linear transformations remove structure in the sense that the number of local extrema (or zero-crossings) in the output signal does not exceed the number of local extrema (or zero-crossings) in the original signal?
    • How should one create a multi-resolution family of representations with the property that a signal at a coarser level of scale never contains more structure than a signal at a finer level of scale?

    It is proposed that there is only one reasonable way to define a scale-space for 1D discrete signals comprising a continuous scale parameter, namely by (discrete) convolution with the family of kernels T(n; t) = e^{-t} I_n(t), where I_n are the modified Bessel functions of integer order. Similar arguments applied in the continuous case uniquely lead to the Gaussian kernel.

    Some obvious discretizations of the continuous scale-space theory are discussed in view of the results presented. It is shown that the kernel T(n; t) arises naturally in the solution of a discretized version of the diffusion equation. The commonly adapted technique with a sampled Gaussian can lead to undesirable effects since scale-space violations might occur in the corresponding representation. The result exemplifies the fact that properties derived in the continuous case might be violated after discretization.

    A two-dimensional theory, showing how the scale-space should be constructed for images, is given based on the requirement that local extrema must not be enhanced, when the scale parameter is increased continuously. In the separable case the resulting scale-space representation can be calculated by separated convolution with the kernel T(n; t).

    The presented discrete theory has computational advantages compared to a scale-space implementation based on the sampled Gaussian, for instance concerning the Laplacian of the Gaussian. The main reason is that the discrete nature of the implementation has been taken into account already in the theoretical formulation of the scale-space representation.

  • 7.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Bayesian Estimation of Beta Mixture Models with Variational Inference2011In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 33, no 11, p. 2160-2173Article in journal (Refereed)
    Abstract [en]

    Bayesian estimation of the parameters in beta mixture models (BMM) is analytically intractable. The numerical solutionsto simulate the posterior distribution are available, but incur high computational cost. In this paper, we introduce an approximation tothe prior/posterior distribution of the parameters in the beta distribution and propose an analytically tractable (closed-form) Bayesianapproach to the parameter estimation. The approach is based on the variational inference (VI) framework. Following the principles ofthe VI framework and utilizing the relative convexity bound, the extended factorized approximation method is applied to approximate thedistribution of the parameters in BMM. In a fully Bayesian model where all the parameters of the BMM are considered as variables andassigned proper distributions, our approach can asymptotically find the optimal estimate of the parameters posterior distribution. Also,the model complexity can be determined based on the data. The closed-form solution is proposed so that no iterative numericalcalculation is required. Meanwhile, our approach avoids the drawback of overfitting in the conventional expectation maximizationalgorithm. The good performance of this approach is verified by experiments with both synthetic and real data.

  • 8. Ma, Zhanyu
    et al.
    Teschendorff, Andrew E.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Qiao, Yuanyuan
    Zhang, Honggang
    Guo, Jun
    Variational Bayesian Matrix Factorization for Bounded Support Data2015In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 37, no 4, p. 876-889Article in journal (Refereed)
    Abstract [en]

    A novel Bayesian matrix factorization method for bounded support data is presented. Each entry in the observation matrix is assumed to be beta distributed. As the beta distribution has two parameters, two parameter matrices can be obtained, which matrices contain only nonnegative values. In order to provide low-rank matrix factorization, the nonnegative matrix factorization (NMF) technique is applied. Furthermore, each entry in the factorized matrices, i.e., the basis and excitation matrices, is assigned with gamma prior. Therefore, we name this method as beta-gamma NMF (BG-NMF). Due to the integral expression of the gamma function, estimation of the posterior distribution in the BG-NMF model can not be presented by an analytically tractable solution. With the variational inference framework and the relative convexity property of the log-inverse-beta function, we propose a new lower-bound to approximate the objective function. With this new lower-bound, we derive an analytically tractable solution to approximately calculate the posterior distributions. Each of the approximated posterior distributions is also gamma distributed, which retains the conjugacy of the Bayesian estimation. In addition, a sparse BG-NMF can be obtained by including a sparseness constraint to the gamma prior. Evaluations with synthetic data and real life data demonstrate the good performance of the proposed method.

  • 9. Rodrigues, Filipe
    et al.
    Borysov, Stanislav S.
    KTH, Centres, Nordic Institute for Theoretical Physics NORDITA. Singapore-MIT Alliance for Research and Technology, Singapore; Stockholm Univ, Roslagstullsbacken 23, SE-10691 Stockholm, Sweden.
    Ribeiro, Bernardete
    Pereira, Francisco C.
    A Bayesian Additive Model for Understanding Public Transport Usage in Special Events2017In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 39, no 11, p. 2113-2126Article in journal (Refereed)
    Abstract [en]

    Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although these are usually planned well in advance, their impact is difficult to predict, even when organisers and transportation operators coordinate. The problem highly increases when several events happen concurrently. To solve these problems, costly processes, heavily reliant on manual search and personal experience, are usual practice in large cities like Singapore, London or Tokyo. This paper presents a Bayesian additive model with Gaussian process components that combines smart card records from public transport with context information about events that is continuously mined from the Web. We develop an efficient approximate inference algorithm using expectation propagation, which allows us to predict the total number of public transportation trips to the special event areas, thereby contributing to a more adaptive transportation system. Furthermore, for multiple concurrent event scenarios, the proposed algorithm is able to disaggregate gross trip counts into their most likely components related to specific events and routine behavior. Using real data from Singapore, we show that the presented model outperforms the best baseline model by up to 26 percent in R-2 and also has explanatory power for its individual components.

  • 10.
    Taghia, Jalil
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES).
    Variational Inference for Watson Mixture Model2016In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 38, no 9, p. 1886-1900Article in journal (Refereed)
    Abstract [en]

    This paper addresses modelling data using the Watson distribution. The Watson distribution is one of the simplest distributions for analyzing axially symmetric data. This distribution has gained some attention in recent years due to its modeling capability. However, its Bayesian inference is fairly understudied due to difficulty in handling the normalization factor. Recent development of Markov chain Monte Carlo (MCMC) sampling methods can be applied for this purpose. However, these methods can be prohibitively slow for practical applications. A deterministic alternative is provided by variational methods that convert inference problems into optimization problems. In this paper, we present a variational inference for Watson mixture models. First, the variational framework is used to side-step the intractability arising from the coupling of latent states and parameters. Second, the variational free energy is further lower bounded in order to avoid intractable moment computation. The proposed approach provides a lower bound on the log marginal likelihood and retains distributional information over all parameters. Moreover, we show that it can regulate its own complexity by pruning unnecessary mixture components while avoiding over-fitting. We discuss potential applications of the modeling with Watson distributions in the problem of blind source separation, and clustering gene expression data sets.

  • 11.
    Taghia, Jalil
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Ma, Zhanyu
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Bayesian Estimation of the von-Mises Fisher Mixture Model with Variational Inference2014In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 36, no 9, p. 1701-1715Article in journal (Refereed)
    Abstract [en]

    This paper addresses the Bayesian estimation of the von-Mises Fisher (vMF) mixture model with variational inference (VI). The learning task in VI consists of optimization of the variational posterior distribution. However, the exact solution by VI does not lead to an analytically tractable solution due to the evaluation of intractable moments involving functional forms of the Bessel function in their arguments. To derive a closed-form solution, we further lower bound the evidence lower bound where the bound is tight at one point in the parameter distribution. While having the value of the bound guaranteed to increase during maximization, we derive an analytically tractable approximation to the posterior distribution which has the same functional form as the assigned prior distribution. The proposed algorithm requires no iterative numerical calculation in the re-estimation procedure, and it can potentially determine the model complexity and avoid the over-fitting problem associated with conventional approaches based on the expectation maximization. Moreover, we derive an analytically tractable approximation to the predictive density of the Bayesian mixture model of vMF distributions. The performance of the proposed approach is verified by experiments with both synthetic and real data.

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