Joint Tensor Feature Analysis For Visual Object Recognition

Wong, Wai Keung; Lai, Zhihui; Xu, Yong; Wen, Jiajun; Ho, Chu Po

doi:10.1109/tcyb.2014.2374452

Cited by 69 publications

(19 citation statements)

References 38 publications

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“…SSC further induces sparsity by utilizing the l 1 Subspace Detection Property [28] in an independent manner, while the LRR model considers the intrinsic relation among the data objects in a holistic way via the low-rank requirement. It has been proved that, when the data set is actually composed of a union of multiple subspaces, the LRR method can reveal this structure through subspace clustering [29]- [33].…”

Section: Introductionmentioning

confidence: 99%

Localized LRR on Grassmann Manifold: An Extrinsic View

Wang

Gao

et al. 2018

IEEE Trans. Circuits Syst. Video Technol.

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Subspace data representation has recently become a common practice in many computer vision tasks. It demands generalizing classical machine learning algorithms for subspace data. Low-Rank Representation (LRR) is one of the most successful models for clustering vectorial data according to their subspace structures. This paper explores the possibility of extending LRR for subspace data on Grassmann manifolds. Rather than directly embedding the Grassmann manifolds into the symmetric matrix space, an extrinsic view is taken to build the LRR self-representation in the local area of the tangent space at each Grassmannian point, resulting in a localized LRR method on Grassmann manifolds. A novel algorithm for solving the proposed model is investigated and implemented. The performance of the new clustering algorithm is assessed through experiments on several real-world datasets including MNIST handwritten digits, ballet video clips, SKIG action clips, DynTex++ dataset and highway traffic video clips. The experimental results show the new method outperforms a number of state-of-the-art clustering methods.

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Section: Introductionmentioning

confidence: 99%

Localized LRR on Grassmann Manifold: An Extrinsic View

Wang

Gao

et al. 2018

IEEE Trans. Circuits Syst. Video Technol.

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“…the 3D Gabor features, SSLBP features, improved SVM classifiers, sparse representation scheme, deep learning scheme etc. introduced in [30–40]) are our further work. Meanwhile, our future work includes exploring the relationship between different levels of fingerprint features and proposing more powerful fusion strategy.…”

Section: Discussionmentioning

confidence: 99%

Case study of 3D fingerprints applications

et al. 2017

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Human fingers are 3D objects. More information will be provided if three dimensional (3D) fingerprints are available compared with two dimensional (2D) fingerprints. Thus, this paper firstly collected 3D finger point cloud data by Structured-light Illumination method. Additional features from 3D fingerprint images are then studied and extracted. The applications of these features are finally discussed. A series of experiments are conducted to demonstrate the helpfulness of 3D information to fingerprint recognition. Results show that a quick alignment can be easily implemented under the guidance of 3D finger shape feature even though this feature does not work for fingerprint recognition directly. The newly defined distinctive 3D shape ridge feature can be used for personal authentication with Equal Error Rate (EER) of ~8.3%. Also, it is helpful to remove false core point. Furthermore, a promising of EER ~1.3% is realized by combining this feature with 2D features for fingerprint recognition which indicates the prospect of 3D fingerprint recognition.

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“…Inspired by these works, 2,1 -norm has been widely used as the regularization term in the criterion function. For example, Wong et al [40] proposed 2,1 -norm based tensor feature selection for image analysis. Gui et al [37] used 2,1 -norm as the regularized term in subspace learning and proposed a joint feature extraction and selection method for data classification.…”

Section: Introductionmentioning

confidence: 99%

Discriminant Analysis via Joint Euler Transform and $\ell_{2,1}$ -Norm

Liao

Gao

Yang

et al. 2018

IEEE Trans. on Image Process.

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Linear Discriminant analysis (LDA) has been widely used for face recognition. However, when identifying faces in the wild, the existence of outliers that deviate significantly from the rest of data can arbitrarily skew the desired solution. This usually deteriorates LDA's performance dramatically, thus preventing it from mass deployment in real-world applications. To handle this problem, we propose an effective distance metric learning method based LDA, namely Euler LDA-L21 (e-LDA-L21). e-LDA-L21 is carried out in two stages, in which each image is mapped into a complex space by Euler transform in the first stage and the ℓ2,1-norm is adopted as the distance metric in the second stage. This not only reveals nonlinear features but also exploits the geometric structure of data. To solve e-LDA-L21 efficiently, we propose an iterative algorithm, which is a closed-form solution at each iteration with convergence guaranteed. Finally, we extend e-LDA-L21 to Euler 2DLDA-L21 (e-2DLDA-L21) which further exploits the spatial information embedded in image pixels. Experimental results on several face databases demonstrate its superiority over the state-of-the-art algorithms.

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Joint Tensor Feature Analysis For Visual Object Recognition

Cited by 69 publications

References 38 publications

Localized LRR on Grassmann Manifold: An Extrinsic View

Localized LRR on Grassmann Manifold: An Extrinsic View

Case study of 3D fingerprints applications

Discriminant Analysis via Joint Euler Transform and $\ell_{2,1}$ -Norm

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