Classification and clustering via dictionary learning with structured incoherence and shared features

By Ignacio Francisco Ramírez Paulino, Pablo Sprechmann, Guillermo Sapiro. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010

802 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f7a42ca0591a24c95f1e41ecf1243c031b8379d0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation (2014, positive): “, of wavelets) has recently been used for several problems related to low-level image processing tasks such as denoising [26] and classification [27], demonstrating the ability of sparse representations to model natural images.”
Learning towards Minimum Hyperspherical Energy (2018, background): “Diversity regularization is shown useful in sparse coding [32, 35], ensemble learning [26, 24], selfpaced learning [21], metric learning [58], etc.”
Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification (2014, methodology): “2013), discriminative KSVD (DKSVD) (Zhang and Li 2010) and dictionary learning with structure incoherence (DLSI) (Ramirez et al. 2010).”
Multipath Sparse Coding Using Hierarchical Matching Pursuit (2013, methodology): “Note that MI-KSVD is quite different from dictionary learning algorithms proposed in [26], which use L2 norm to measure mutual incoherence and L1 norm to enforce sparsity.”
Sparse Dictionary Learning for Edit Propagation of High-Resolution Images (2014, methodology): “In the same way, we can estimate the similarity of two samples in Dinit by comparing the number of signals using them, and their contributions in the sparse decomposition [24].”
Generalized Domain-Adaptive Dictionaries (2013, background): “Recent advances in learning discriminative dictionaries [20, 28] suggest that learning class-wise, mutually incoherent dictionaries works better for discrimination.”
Fast and Robust Archetypal Analysis for Representation Learning (2014, methodology): “We ﬁrst show that it performs as well as sparse coding for learning codebooks of features in visual recognition tasks [24] and for signal classiﬁcation [14, 21, 23].”
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (2015, methodology): “…with dictionary-based methods , SJTAD and RDDL. SJTAD is an implementation of the method in [16], whereas the discriminative dictionary learning of [13] is used for RDDL. Finally to showcase the strengths of our design choice of sparse representation using discriminative dictionary learning, we…”
See all by looking at a few: Sparse modeling for finding representative objects (2012, background): “In this section, we formulate the problem of finding representative objects from the collection of data points.”
Face recognition based on adaptive margin and diversity regularization constraints (2020, background): “Diversity regularization is widely used in sparse coding [18], ensemble learning [19], metric learning [20] etc.”
Fisher Discrimination Dictionary Learning for sparse representation (2011, methodology): “Recently sparse representation techniques have also led to promising results in image classification, e.g. face recognition (FR) [5] [6] [7] [10] [31], digit and texture classification [8] [9] [11] [12], etc.”
Learning Sparsifying Transforms (2013, methodology): “When a sparsifying transform is known for the signal , the process of obtaining a sparse code of given sparsity involves solving the following problem, which we call the transform sparse coding problem.”
Regularizing Neural Networks via Minimizing Hyperspherical Energy (2019, background): “Diversity-based regularization has been found useful in sparse coding [20, 21], ensemble learning [22, 23], self-paced learning [24], metric learning [25], latent variable models [26], etc. Early studies in sparse coding [20, 21] model the diversity with the empirical covariance matrix and show…”
Projective dictionary pair learning for pattern classification (2014, methodology): “Training time (s) Testing time (s) NSC 94.7 no need 1.41e-3 SVM 95.6 0.70 3.49e-5 CRC 97.0 no need 1.92e-3 SRC 96.5 no need 2.16e-2 DLSI(C) 97.0 567.47 4.30e-2 FDDL 96.7 6,574.6 1.43 LC-KSVD 96.7 412.58 4.22e-4 MMDL 97.3 - - DPL 97.5 4.38 1.71e-4 Extended YaleB database The recognition accuracies an”
Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps (2013, methodology): “We refer to the corresponding algorithms as K-SVD, ScTPM and DLSI for simplicity.”