Results 31 to 40 of about 224,871 (275)
CoRR, 2012 We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic topic models, STC relaxes the normalization constraint of admixture proportions and the constraint of defining a normalized likelihood function.
Jun Zhu 0001, Eric P. Xing
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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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An Optimum Deraining Scheme using Sparse Coding
International Journal of Emerging Research in Engineering, Science, and Management, 2022 Rain streak removal is a challenging and interesting task of image processing where the rain streaks will be removed from an image with rain streaks. In the literature, a large number of proposals are made where rain streak removal is considered as image
A Hazarathaiah +4 more
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Sparse Regression Codes for Multi-terminal Source and Channel Coding [PDF]
, 2012 We study a new class of codes for Gaussian multi-terminal source and channel coding. These codes are designed using the statistical framework of high-dimensional linear regression and are called Sparse Superposition or Sparse Regression codes.
Tatikonda, Sekhar, Venkataramanan, Ramji
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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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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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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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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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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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