Improvement of Supervised Shape Retrieval by Learning the Manifold Space

No abstract is available for this paper.

• Publication year

  2011

• Venue

  Iranian Conference on Machine Vision and Image Processing

• Publication date

  2011-11-01

• Fields of study

  Mathematics, Computer Science, Engineering

• Identifiers
  DOI 10.1109/IRANIANMVIP.2011.6121605
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Nonlinear Dimensionality Reduction

  2021cited by this paper
• Shape retrieval based on manifold learning by fusion of dissimilarity measures

  2012cited by this paper
• Learning the shape manifold to improve object recognition

  2012cited by this paper
• Binary Image 2D Shape Learning and Recognition Based on Lattice-Computing (LC) Techniques

  2011cited by this paper
• Facial expression recognition on multiple manifolds

  2011cited by this paper
• Shape recognition based on Kernel-edit distance

  2010cited by this paper
• Shape Classification Using Tree -Unions

  2010cited by this paper
• Manifold embedding for shape analysis

  2010cited by this paper
• Context based object categorization: A critical survey

  2010cited by this paper
• Learning an Intrinsic-Variable Preserving Manifold for Dynamic Visual Tracking

  2010cited by this paper
• Incremental Embedding and Learning in the Local Discriminant Subspace With Application to Face Recognition

  2010cited by this paper
• Synchronized Submanifold Embedding for Person-Independent Pose Estimation and Beyond

  2009cited by this paper
• Human pose estimation from corrupted silhouettes using a sub-manifold voting strategy in latent variable space

  2009cited by this paper
• Multi-view face recognition based on tensor subspace analysis and view manifold modeling

  2009cited by this paper
• Natural facial expression recognition using differential-AAM and manifold learning

  2009cited by this paper
• Learning a Maximum Margin Subspace for Image Retrieval

  2008cited by this paper
• A Survey of Shape Feature Extraction Techniques

  2008cited by this paper
• Skeleton-Based Shape Classification Using Path Similarity

  2008cited by this paper
• Visual learning and recognition of sequential data manifolds with applications to human movement analysis

  2008cited by this paper
• The out-of-sample problem for classical multidimensional scaling

  2008cited by this paper
• Shape retrieval using triangle-area representation and dynamic space warping

  2007cited by this paper
• Learning and Matching of Dynamic Shape Manifolds for Human Action Recognition

  2007cited by this paper
• Hidden Markov Model-Based Weighted Likelihood Discriminant for 2-D Shape Classification

  2007cited by this paper
• An information-theoretic approach to face recognition from face motion manifolds

  2006cited by this paper
• Occlusion Resistant Shape Classifier based onWarped Optimal Path Matching

  2006cited by this paper
• Classification of contour shapes using class segment sets

  2005cited by this paper
• Visual tracking and recognition using probabilistic appearance manifolds

  2005cited by this paper
• Semantic manifold learning for image retrieval

  2005cited by this paper
• Semantic knowledge extraction and annotation for web images

  2005cited by this paper
• Learning an image manifold for retrieval

  2004cited by this paper
• Investigating hidden Markov models' capabilities in 2D shape classification

  2004cited by this paper
• Shape representation and classification using the Poisson equation

  2004cited by this paper
• Similarity-based classification of sequences using hidden Markov models

  2004cited by this paper
• Review of shape representation and description techniques

  2004cited by this paper
• Manifold Based Analysis of Facial Expression

  2004cited by this paper
• Recognition of shapes by editing their shock graphs

  2004cited by this paper
• Principal manifolds and nonlinear dimensionality reduction via tangent space alignment

  2004cited by this paper
• Unsupervised Learning of Image Manifolds by Semidefinite Programming

  2004cited by this paper
• Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data

  2003cited by this paper
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

  2003cited by this paper
• A robust non-linear projection method

  2000cited by this paper
• Shape Similarity Measure Based on Correspondence of Visual Parts

  2000cited by this paper
• Nonlinear dimensionality reduction by locally linear embedding.

  2000influential reference
• A global geometric framework for nonlinear dimensionality reduction.

  2000cited by this paper
• Neural Networks: A Comprehensive Foundation

  1998cited by this paper
• Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets

  1997cited by this paper
• Extensions of Lipschitz mappings into Hilbert space

  1984cited by this paper
• A Nonlinear Mapping for Data Structure Analysis

  1969cited by this paper
• Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis

  1964cited by this paper
• Digital Object Identifier (DOI) 10.1007/s00530-002-0075-y Multimedia Systems © Springer-Verlag 2003

  year unknowncited by this paper

• A Survey on semi-supervised feature selection methods

  2017cites this paper
• Unsupervised manifold learning based on multiple feature spaces

  2014cites this paper
• Shape classification by manifold learning in multiple observation spaces

  2014cites this paper