Statistical binary patterns and post-competitive representation for pattern recognition

Borgi, Mohamed Anouar; Nguyễn, Thanh Phương; Labate, Demetrio; Amar, Chokri Ben

doi:10.1007/s13042-016-0625-9

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…[52] is a local metric, which is particularly robust to complex noise [39] and non-Gaussian noise [52]. It has been widely used in the field of computer vision, such as face clustering [4], object image clustering [3], feature selection [14], and semi-supervised learning [34]. The correntropy [16,52] of arbitrary variables w and v is formulated as where g (•) denotes kernel function, and is the width of…”

Section: Robust Rank-constrained Block Diagonal Subspace Clusteringmentioning

confidence: 99%

Correntropy metric-based robust low-rank subspace clustering for motion segmentation

Zhang

Liu

et al. 2021

Int. J. Mach. Learn. & Cyber.

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The subspace clustering methods for motion segmentation are widely used in the field of computer vision. However, the existing methods ignore the low-rank property of motion trajectory with nonlinear structure and are sensitive to non-Gaussian noise. To this end, we seek to improve the performance of motion segmentation by effectively modeling some important characteristics of the motion trajectories, such as nonlinear structure and contained non-Gaussian noise. Specifically, we propose to use kernel function to model motion trajectory, design a variant of the correntropy-induced metric to measure noise, and integrate the block diagonal regularizer into the kernel subspace clustering to strengthen the block diagonal structure of the learned affinity matrix. More importantly, we propose a unified rank-constrained block diagonal subspace clustering method for motion segmentation, which can handle not only rigid body motion segmentation, but also non-rigid motion segmentation. And we further extend this method to deal with various noises in motion data, such as missing trajectories, corrupted trajectory and outlying trajectory. An effective algorithm HQ& AM, which is integrated by Half-quadratic theory and alternating minimization, is designed to optimize these models. Experimental results on several commonly used motion datasets indicate the effectualness and robustness of our methods.

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Section: Robust Rank-constrained Block Diagonal Subspace Clusteringmentioning

confidence: 99%

Correntropy metric-based robust low-rank subspace clustering for motion segmentation

Zhang

Liu

et al. 2021

Int. J. Mach. Learn. & Cyber.

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show abstract

“…The experimental results in Sec. 4 show that the discriminative structure information captured by the multi-scale block framework makes sense in classification.…”

Section: Introductionmentioning

confidence: 98%

“…The face recognition system integrates a variety of professional technologies, such as artificial intelligence, image recognition, machine learning, model theory, and expert systems 1 – 4 In recent years, lots of face detection methods have been proposed and achieve good performance under controlled conditions. According to the differences of feature extraction methods and feature forms, these face detection methods could be separated into two categories: global feature extraction and local feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Local image descriptor developed from Fechner’s law

Gao

Guo

et al. 2023

J. Electron. Imag.

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.Human perception of visual stimulus and its physical characteristics have a nonlinear-logarithmic relation as stated in Fechner’s law, which is a psychophysical law of perception (i.e., subjective sensation). Inspired by Fechner’s law, we use the salient differences among pixels in images as the physical characteristics to mimic the human pattern perception and propose a Fechner local image descriptor (FLID) for face image representation. FLID is a nonlinear descriptor, so the represented image is more hierarchical than the general linear one. In addition, a threshold method is used to divide the features into four different intervals, which can effectively reduce the effects of noise and illumination. However, considering that FLID only extracts the local features of single-scale blocks, we incorporate multi-scale block into FLID and propose a multi-scale block Fechner local image descriptor (MB-FLID) to extract more global structural features. In the experimental part, we verified the performance of FLID and MB-FLID on the Face Recognition Technology (FERET), extended Yale-B, Olivetti Research Laboratories, Yale, and AR face databases, and compared them with a host of other local feature descriptors. The experimental results show that the proposed FLID and MB-FLID outperform the compared descriptors.

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Neighborhood and center difference-based-LBP for face recognition

Karanwal

Diwakar

2021

Pattern Anal Applic

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Statistical binary patterns and post-competitive representation for pattern recognition

Cited by 5 publications

References 52 publications

Correntropy metric-based robust low-rank subspace clustering for motion segmentation

Correntropy metric-based robust low-rank subspace clustering for motion segmentation

Local image descriptor developed from Fechner’s law

Neighborhood and center difference-based-LBP for face recognition

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