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    Article

    Deep fused two-step cross-modal hashing with multiple semantic supervision

    Existing cross-modal hashing methods ignore the informative multimodal joint information and cannot fully exploit the semantic labels. In this paper, we propose a deep fused two-step cross-modal hashing (DFTH)...

    Peipei Kang, Zehang Lin, Zhenguo Yang in Multimedia Tools and Applications (2022)

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    Article

    Intra-class low-rank regularization for supervised and semi-supervised cross-modal retrieval

    Cross-modal retrieval aims to retrieve related items across different modalities, for example, using an image query to retrieve related text. The existing deep methods ignore both the intra-modal and inter-mod...

    Peipei Kang, Zehang Lin, Zhenguo Yang, **aozhao Fang in Applied Intelligence (2022)

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    Article

    Equi-affine Invariant Geometry for Shape Analysis

    Traditional models of bendable surfaces are based on the exact or approximate invariance to deformations that do not tear or stretch the shape, leaving intact an intrinsic geometry associated with it. These ge...

    Dan Raviv, Alexander M. Bronstein in Journal of Mathematical Imaging and Vision (2014)

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    Article

    A Gromov-Hausdorff Framework with Diffusion Geometry for Topologically-Robust Non-rigid Shape Matching

    In this paper, the problem of non-rigid shape recognition is studied from the perspective of metric geometry. In particular, we explore the applicability of diffusion distances within the Gromov-Hausdorff fram...

    Alexander M. Bronstein, Michael M. Bronstein in International Journal of Computer Vision (2010)

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    Article

    Nonlinear Dimensionality Reduction by Topologically Constrained Isometric Embedding

    Many manifold learning procedures try to embed a given feature data into a flat space of low dimensionality while preserving as much as possible the metric in the natural feature space. The embedding process u...

    Guy Rosman, Michael M. Bronstein in International Journal of Computer Vision (2010)

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    Article

    Full and Partial Symmetries of Non-rigid Shapes

    Symmetry and self-similarity are the cornerstone of Nature, exhibiting themselves through the shapes of natural creations and ubiquitous laws of physics. Since many natural objects are symmetric, the absence o...

    Dan Raviv, Alexander M. Bronstein in International Journal of Computer Vision (2010)

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    Article

    Partial Similarity of Objects, or How to Compare a Centaur to a Horse

    Similarity is one of the most important abstract concepts in human perception of the world. In computer vision, numerous applications deal with comparing objects observed in a scene with some a priori known patte...

    Alexander M. Bronstein, Michael M. Bronstein in International Journal of Computer Vision (2009)

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    Article

    Topology-Invariant Similarity of Nonrigid Shapes

    This paper explores the problem of similarity criteria between nonrigid shapes. Broadly speaking, such criteria are divided into intrinsic and extrinsic, the first referring to the metric structure of the obje...

    Alexander M. Bronstein, Michael M. Bronstein in International Journal of Computer Vision (2009)

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    Article

    Analysis of Two-Dimensional Non-Rigid Shapes

    Analysis of deformable two-dimensional shapes is an important problem, encountered in numerous pattern recognition, computer vision and computer graphics applications. In this paper, we address three major pr...

    Alexander M. Bronstein, Michael M. Bronstein in International Journal of Computer Vision (2008)

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    Article

    Three-Dimensional Face Recognition

    An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invar...

    Alexander M. Bronstein, Michael M. Bronstein in International Journal of Computer Vision (2005)

  11. Article

    Biometrics was no match for hair-raising tricks

    People have been fooling the latest thing in security for a very long time.

    Michael M. Bronstein, Alexander M. Bronstein in Nature (2002)