Publications

Topics:
  1. P. Sprechmann, R. Litman, T. Ben Yakar, A. M. Bronstein, G. Sapiro, Efficient supervised sparse analysis and synthesis operators, Proc. Neural Information Proc. Systems (NIPS), 2013 details

    Efficient supervised sparse analysis and synthesis operators

    P. Sprechmann, R. Litman, T. Ben Yakar, A. M. Bronstein, G. Sapiro
    Proc. Neural Information Proc. Systems (NIPS), 2013

    In this paper, we propose a new and computationally efficient framework for learning sparse models. We formulate a unified approach that contains as particular cases models promoting sparse synthesis and analysis type of priors, and mixtures thereof. The supervised training of the proposed model is formulated as a bilevel optimization problem, in which the operators are optimized to achieve the best possible performance on a specific task, e.g., reconstruction or classification. By restricting the operators to be shift invariant, our approach can be thought as a way of learning analysis+synthesis sparsity-promoting convolutional operators. Leveraging recent ideas on fast trainable regressors designed to approximate exact sparse codes, we propose a way of constructing feed-forward neural networks capable of approximating the learned models at a fraction of the computational cost of exact solvers. In the shift-invariant case, this leads to a principled way of constructing task-specific convolutional networks. We illustrate the proposed models on several experiments in music analysis and image processing applications.

    T. Ben Yakar, R. Litman, P. Sprechmann, A. M. Bronstein, G. Sapiro, Bilevel sparse models for polyphonic music transcription, Proc. Annual Conf. of the Int'l Society for Music Info. Retrieval (ISMIR), 2013 details

    Bilevel sparse models for polyphonic music transcription

    T. Ben Yakar, R. Litman, P. Sprechmann, A. M. Bronstein, G. Sapiro
    Proc. Annual Conf. of the Int'l Society for Music Info. Retrieval (ISMIR), 2013

    In this work, we propose a trainable sparse model for automatic polyphonic music transcription, which incorporates several successful approaches into a unified optimization framework. Our model combines unsupervised synthesis models similar to latent component analysis and nonnegative factorization with metric learning techniques that allow supervised discriminative learning. We develop efficient stochastic gradient training schemes allowing unsupervised, semi-, and fully supervised training of the model as well its adaptation to test data. We show efficient fixed complexity and latency approximation that can replace iterative minimization algorithms in time-critical applications. Experimental evaluation on synthetic and real data shows promising initial results.

    J. Pokrass, A. M. Bronstein, M. M. Bronstein, P. Sprechmann, G. Sapiro, Sparse modeling of intrinsic correspondences, Computer Graphics Forum (CGF), Vol. 32(2), 2013 details

    Sparse modeling of intrinsic correspondences

    J. Pokrass, A. M. Bronstein, M. M. Bronstein, P. Sprechmann, G. Sapiro
    Computer Graphics Forum (CGF), Vol. 32(2), 2013

    We present a novel sparse modeling approach to non-rigid shape matching using only the ability to detect repeatable regions. As the input to our algorithm, we are given only two sets of regions in two shapes; no descriptors are provided so the correspondence between the regions is not know, nor we know how many regions correspond in the two shapes. We show that even with such scarce information, it is possible to establish very accurate correspondence between the shapes by using methods from the field of sparse modeling, being this, the first non-trivial use of sparse models in shape correspondence. We formulate the problem of permuted sparse coding, in which we solve simultaneously for an unknown permutation ordering the regions on two shapes and for an unknown correspondence in functional representation. We also propose a robust variant capable of handling incomplete matches. Numerically, the problem is solved efficiently by alternating the solution of a linear assignment and a sparse coding problem. The proposed methods are evaluated qualitatively and quantitatively on standard benchmarks containing both synthetic and scanned objects.

    A. Kovnatsky, M. M. Bronstein, A. M. Bronstein, K. Glashoff, R. Kimmel, Coupled quasi-harmonic bases, Computer Graphics Forum (CGF), Vol. 32(2), 2013 details

    Coupled quasi-harmonic bases

    A. Kovnatsky, M. M. Bronstein, A. M. Bronstein, K. Glashoff, R. Kimmel
    Computer Graphics Forum (CGF), Vol. 32(2), 2013

    State-of-the-art approaches to shape analysis, synthesis, and correspondence rely on these natural harmonic bases that allow using classical tools from harmonic analysis on manifolds. However, many applications involving multiple shapes are obstacled by the fact that Laplacian eigenbases computed independently on different shapes are often incompatible with each other. In this paper, we propose the construction of common approximate eigenbases for multiple shapes using approximate joint diagonalization algorithms, taking as input a set of corresponding functions (e.g. indicator functions of stable regions) on the two shapes. We illustrate the benefits of the proposed approach on tasks from shape editing, pose transfer, correspondence, and similarity.

    P. Sprechmann, A. M. Bronstein, J.-M. Morel, G. Sapiro, Audio restoration from multiple copies, Proc. Int'l Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2013 details

    Audio restoration from multiple copies

    P. Sprechmann, A. M. Bronstein, J.-M. Morel, G. Sapiro
    Proc. Int'l Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2013

    A method for removing impulse noise from audio signals by fusing multiple copies of the same recording is introduced in this paper. The proposed algorithm exploits the fact that while in general multiple copies of a given recording are available, all sharing the same master, most degradations in audio signals are record-dependent. Our method first seeks for the optimal non-rigid alignment of the signals that is robust to the presence of sparse outliers with arbitrary magnitude. Unlike previous approaches, we simultaneously find the optimal alignment of the signals and impulsive degradation. This is obtained via continuous dynamic time warping computed solving an Eikonal equation. We propose to use our approach in the derivative domain, reconstructing the signal by solving an inverse problem that resembles the Poisson image editing technique. The proposed framework is here illustrated and tested in the restoration of old gramophone recordings showing promising results; however, it can be used in other application where different copies of the signal of interest are available and the degradations are copy-dependent.

    P. Sprechmann, A. M. Bronstein, M. M. Bronstein, G. Sapiro, Learnable low rank sparse models for speech denoising, Proc. Int'l Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2013 details

    Learnable low rank sparse models for speech denoising

    P. Sprechmann, A. M. Bronstein, M. M. Bronstein, G. Sapiro
    Proc. Int'l Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2013

    In this paper we present a framework for real time enhancement of speech signals. Our method leverages a new process-centric approach for sparse and parsimonious models, where the representation pursuit is obtained applying a deterministic function or process rather than solving an optimization problem. We first propose a rank-regularized robust version of non-negative matrix factorization (NMF) for modeling time-frequency representations of speech signals in which the spectral frames are decomposed as sparse linear combinations of atoms of a low-rank dictionary. Then, a parametric family of pursuit processes is derived from the iteration of the proximal descent method for solving this model. We present several experiments showing successful results and the potential of the proposed framework. Incorporating discriminative learning makes the proposed method significantly outperform exact NMF algorithms, with fixed latency and at a fraction of it’s computational complexity.

    A. Kovnatski, D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Geometric and photometric data fusion in non-rigid shape analysis, Numerical Mathematics: Theory, Methods and Applications (NM-TMA), Vol. 6(1), 2013 details

    Geometric and photometric data fusion in non-rigid shape analysis

    A. Kovnatski, D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel
    Numerical Mathematics: Theory, Methods and Applications (NM-TMA), Vol. 6(1), 2013

    In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local and global shape descriptors. Our construction is based on the definition of a diffusion process on the shape manifold embedded into a high-dimensional space where the embedding coordinates represent the photometric information. Experimental results show that such data fusion is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fail.

    J. Pokrass, A. M. Bronstein, M. M. Bronstein, Partial shape matching without point-wise correspondence, Numerical Mathematics: Theory, Methods and Applications (NM-TMA), Vol. 6(1), 2013 details

    Partial shape matching without point-wise correspondence

    J. Pokrass, A. M. Bronstein, M. M. Bronstein
    Numerical Mathematics: Theory, Methods and Applications (NM-TMA), Vol. 6(1), 2013

    Partial similarity of shapes in a challenging problem arising in many important applications in computer vision, shape analysis, and graphics, e.g. when one has to deal with partial information and acquisition artifacts. The problem is especially hard when the underlying shapes are non-rigid and are given up to a deformation. Partial matching is usually approached by computing local descriptors on a pair of shapes and then establishing a point-wise non-bijective correspondence between the two, taking into account possibly different parts. In this paper, we introduce an alternative correspondence-less approach to matching fragments to an entire shape undergoing a non-rigid deformation. We use diffusion geometric descriptors and optimize over the integration domains on which the integral descriptors of the two parts match. The problem is regularized using the Mumford-Shah functional. We show an efficient discretization based on the Ambrosio-Tortorelli approximation generalized to triangular meshes and point clouds, and present experiments demonstrating the success of the proposed method.

    R. Litman, and A. M. Bronstein, Learning spectral descriptors for deformable shape correspondence, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 36(1), 2013 details

    Learning spectral descriptors for deformable shape correspondence

    R. Litman, and A. M. Bronstein
    IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 36(1), 2013

    Informative and discriminative feature descriptors play a fundamental role in deformable shape analysis. For example, they have been successfully employed in correspondence, registration, and retrieval tasks. In the recent years, significant attention has been devoted to descriptors obtained from the spectral decomposition of the Laplace-Beltrami operator associated with the shape. Notable examples in this family are the heat kernel signature (HKS) and the recently introduced wave kernel signature (WKS). Laplacian-based descriptors achieve state-of-the-art performance in numerous shape analysis tasks; they are computationally efficient, isometry-invariant by construction, and can gracefully cope with a variety of transformations. In this paper, we formulate a generic family of parametric spectral descriptors. We argue that in order to be optimized for a specific task, the descriptor should take into account the statistics of the corpus of shapes to which it is applied (the “signal”) and those of the class of transformations to which it is made insensitive (the “noise”). While such statistics are hard to model axiomatically, they can be learned from examples. Following the spirit of the Wiener filter in signal processing, we show a learning scheme for the construction of optimized spectral descriptors and relate it to Mahalanobis metric learning. The superiority of the proposed approach in generating correspondences is demonstrated on synthetic and scanned human figures. We also show that the learned descriptors are robust enough to be learned on synthetic data and transferred successfully to scanned shapes.