Results 41 to 50 of about 927,730 (287)
Semi-supervised sparse coding [PDF]
2014 International Joint Conference on Neural Networks (IJCNN), 2014 Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled.
Jim Jing-Yan Wang, Xin Gao 0001
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A weighted block cooperative sparse representation algorithm based on visual saliency dictionary
CAAI Transactions on Intelligence Technology, 2023 Unconstrained face images are interfered by many factors such as illumination, posture, expression, occlusion, age, accessories and so on, resulting in the randomness of the noise pollution implied in the original samples.
Rui Chen +4 more
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Representation Learning via Cauchy Convolutional Sparse Coding
IEEE Access, 2021 In representation learning, Convolutional Sparse Coding (CSC) enables unsupervised learning of features by jointly optimising both an $\ell _{2}$ -norm fidelity term and a sparsity enforcing penalty.
Perla Mayo +3 more
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Sparse codes as Alpha Matte [PDF]
Proceedings of the British Machine Vision Conference 2014, 2014 In this paper, image matting is cast as a sparse coding problem wherein the sparse codes directly give the estimate of the alpha matte. Hence, there is no need to use the matting equation that restricts the estimate of alpha from a single pair of foreground (F) and background (B) samples.
Johnson, Jubin +2 more
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Sparse representation of salient regions for no-reference image quality assessment
International Journal of Advanced Robotic Systems, 2016 This paper introduces an efficient feature learning framework via sparse coding for no-reference image quality assessment. The important part of the proposed framework is based on sparse feature extraction from a sparse representation matrix, which is ...
Tianpeng Feng +5 more
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Local structure preserving sparse coding for infrared target recognition. [PDF]
PLoS ONE, 2017 Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex.
Jing Han +3 more
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Mathematics, 2022 Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC ...
Bing Zhang, Jizhong Liu
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An Improved Robust Sparse Coding for Face Recognition with Disguise
International Journal of Advanced Robotic Systems, 2012 Robust vision-based face recognition is one of most challenging tasks for robots. Recently the sparse representation-based classification (SRC) has been proposed to solve the problem.
Dexing Zhong +3 more
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Transformational Sparse Coding
CoRR, 2017 A fundamental problem faced by object recognition systems is that objects and their features can appear in different locations, scales and orientations. Current deep learning methods attempt to achieve invariance to local translations via pooling, discarding the locations of features in the process.
Dimitrios C. Gklezakos, Rajesh P. N. Rao
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Parametric dictionary design for sparse coding [PDF]
, 2009 —This paper introduces a new dictionary design method for sparse coding of a class of signals. It has been shown that one can sparsely approximate some natural signals using an overcomplete set of parametric functions, e.g. [1], [2].
Yaghoobi, M. +5 more
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