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Your search keyword '"Wu, Mingna"' showing total 2 results
Start Over Author "Wu, Mingna" Publisher elsevier b.v.
2 results on '"Wu, Mingna"'

1. Simultaneous learning of reduced prototypes and local metric for image set classification.

Author

Ren, Zhenwen, Wu, Bin, Sun, Quansen, and Wu, Mingna

Subjects
*EXPERT systems, *ERROR probability, *PROTOTYPES, *OBJECT recognition (Computer vision), *MEDICAL technology, *CLASSIFICATION
Abstract

• A simultaneous prototypes and local metric learning algorithm is proposed. • The approach is used in biometrics and object recognition based on image set. • Learned prototypes dramatically reduces the storage and time costs. • Learned local metric significantly improves the discrimination ability of a set. • Experimental results of our method superior/comparable to the literatures. Classification based on image set is recently a competitive technique, where each set contains multiple images of a person or an object. As a widely used model, affine hull has shown its power in modeling image set. However, due to the existence of noise and outliers, the over-large affine hull usually matches fails when two hulls overlapped. Aiming at alleviating this handicap, this paper proposes a novel method for image set classification, namely Learning of Reduced Prototypes and Local Metric (LRPLM). Specifically, for each gallery image set, a reduced set of prototypes and an optimal local feature-wise metric are simultaneously learned, which jointly minimize the loss function involved the estimation of classification error probability. In doing so, LRPLM inherits the merits of affine hull with better representation to account for the unseen appearances and makes use of the powerful discriminative ability improved by the local metric. It looks like that LRPLM pulls similar image sets with the same class label "closer" to each other, while pushing dissimilar ones "far away". Extensive experiments illustrate the considerable effectiveness of LRPLM on three widely used datasets. As we know, classification is a research hotspot in expert and intelligent systems. Different from the previous classification methods, LRPLM focuses on image set-based classification technology, while most of them are single-shot classification technology. Thus, the proposed method can be considered as an expert system technology for medical diagnosis, security monitoring, object categorization, and biometrics recognition applications. [ABSTRACT FROM AUTHOR]

Published
2019
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2. Joint correntropy metric weighting and block diagonal regularizer for robust multiple kernel subspace clustering.

Author

Yang, Chao, Ren, Zhenwen, Sun, Quansen, Wu, Mingna, Yin, Maowei, and Sun, Yuan

Subjects
*SUBSPACES (Mathematics), *ROBUST control, *KERNEL (Mathematics), *RANDOM noise theory, *SELF-expression
Abstract

• We propose a multiple kernel subspace clustering method. • We propose a correntropy metric based multiple kernel weighting strategy. • We use kernel trick to handle non-linear data. • We use block diagonal regularizer to perfect affinity matrix. • Our method gives better results than classical single/multiple kernel methods. Nonlinear kernel-based subspace clustering methods that can reveal the multi-cluster nonlinear structure of samples are an emerging research topic. However, the existing kernel subspace clustering methods have the following three flaws: 1) their clustering performance is largely determined by the chosen kernel function; 2) they may lack robustness in the presence of non-Gaussian noise and impulsive noise; and 3) their learned affinity matrix can not hold the desired block diagonal property for clustering purpose, which possibly leads to incorrect clustering when using spectral clustering. In this paper, we propose a Joint Robust Multiple Kernel Subspace Clustering (JMKSC) method for data clustering, which has two primary innovations. First, our multiple kernel weighting strategy introduces the correntropy metric weighting instead of a fixed, or inappropriately assigned weighting, which is more robust to the non-Gaussian noise and contributes to learning the optimal consensus kernel. Second, our method encourages acquiring an affinity matrix with the optimal block diagonal property based on the block diagonal regularizer (BDR) and the self-expressiveness property. Experiments on several different types of datasets confirm that the proposed JMKSC significantly outperforms several state-of-the-art single kernel and multiple kernel subspace clustering methods in terms of accuracy, NMI and purity. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
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