Using discriminant eigenfeatures for image retrieval

Swets, D.L.; Weng, Juyang

doi:10.1109/34.531802

Cited by 1,436 publications

(655 citation statements)

References 16 publications

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“…However, because of the small number of data sets compared with the dimensionality of the images, an FDA analysis directly on the original data would result in a singular within-class scatter matrix, which cannot be inverted as required in equation (3). Therefore, the FDA is applied to the PC scores resulting from projecting the original data onto the PCs of the PCA subspace used for the analysis (Swets and Weng, 1996;Markiewicz et al, 2009), rather than the original data.…”

Section: Fisher Discriminant Analysismentioning

confidence: 99%

Optimized Data Preprocessing for Multivariate Analysis Applied to ^99mTc-ECD SPECT Data Sets of Alzheimer's Patients and Asymptomatic Controls

Merhof

Markiewicz

Platsch

et al. 2010

J Cereb Blood Flow Metab

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Multivariate image analysis has shown potential for classification between Alzheimer's disease (AD) patients and healthy controls with a high-diagnostic performance. As image analysis of positron emission tomography (PET) and single photon emission computed tomography (SPECT) data critically depends on appropriate data preprocessing, the focus of this work is to investigate the impact of data preprocessing on the outcome of the analysis, and to identify an optimal data preprocessing method. In this work, technetium-99methylcysteinatedimer ( 99m Tc-ECD) SPECT data sets of 28 AD patients and 28 asymptomatic controls were used for the analysis. For a series of different data preprocessing methods, which includes methods for spatial normalization, smoothing, and intensity normalization, multivariate image analysis based on principal component analysis (PCA) and Fisher discriminant analysis (FDA) was applied. Bootstrap resampling was used to investigate the robustness of the analysis and the classification accuracy, depending on the data preprocessing method. Depending on the combination of preprocessing methods, significant differences regarding the classification accuracy were observed. For 99m Tc-ECD SPECT data, the optimal data preprocessing method in terms of robustness and classification accuracy is based on affine registration, smoothing with a Gaussian of 12 mm full width half maximum, and intensity normalization based on the 25% brightest voxels within the whole-brain region.

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Section: Fisher Discriminant Analysismentioning

confidence: 99%

Optimized Data Preprocessing for Multivariate Analysis Applied to ^99mTc-ECD SPECT Data Sets of Alzheimer's Patients and Asymptomatic Controls

Merhof

Markiewicz

Platsch

et al. 2010

J Cereb Blood Flow Metab

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“…In particular, if S B has a nontrivial projection along these directions, LDA considers the corresponding classes to be perfectly separable. Techniques proposed to solve this classical problem include the perturbation method [31], two stage PCA+LDA [26], and the null space methods pioneered by Chen et al [7]. The latter have dominated research in recent years.…”

Section: Seeking Optimal Discriminant Subspace With Kernel Trickmentioning

confidence: 99%

Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition

Tan

Triggs

Analysis and Modeling of Faces and Gestures

230

139

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Abstract. Extending recognition to uncontrolled situations is a key challenge for practical face recognition systems. Finding efficient and discriminative facial appearance descriptors is crucial for this. Most existing approaches use features of just one type. Here we argue that robust recognition requires several different kinds of appearance information to be taken into account, suggesting the use of heterogeneous feature sets. We show that combining two of the most successful local face representations, Gabor wavelets and Local Binary Patterns (LBP), gives considerably better performance than either alone: they are complimentary in the sense that LBP captures small appearance details while Gabor features encode facial shape over a broader range of scales. Both feature sets are high dimensional so it is beneficial to use PCA to reduce the dimensionality prior to normalization and integration. The Kernel Discriminative Common Vector method is then applied to the combined feature vector to extract discriminant nonlinear features for recognition. The method is evaluated on several challenging face datasets including FRGC 1.0.4, FRGC 2.0.4 and FERET, with promising results.

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“…Let us assume that A and B represent the null spaces ofS b andS w respectively, while A = R J − A and B = R J − B denote the orthogonal complements of A and B. Traditional approaches attempt to solve the problem by utilizing an intermediate PCA step to remove A and B. LDA is then performed in the lower dimensional PCA subspace, as it was done for example in [3,29]. Nevertheless, it should be noted at this point that the maximum of the ratio in Eq.6 can be reached only when Ψ TS wΨ = 0 and Ψ TS bΨ = 0.…”

Section: Where Are the Optimal Discriminant Features?mentioning

confidence: 99%

“…To address the problem, one popular approach is to introduce an intermediate Principal Component Analysis (PCA) step to remove the null spaces of the two scatter matrices. LDA is then performed in the lower dimensional PCA subspace, as it was done for example in [3,29]. However, it has been shown that the discarded null spaces may contain significant discriminatory information [10].…”

Section: Introductionmentioning

confidence: 99%

Kernel Discriminant Learning with Application to Face Recognition

Venetsanopoulos

Rogers

2005

Support Vector Machines: Theory and Applications

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Abstract. When applied to high-dimensional pattern classification tasks such as face recognition, traditional kernel discriminant analysis methods often suffer from two problems: 1) small training sample size compared to the dimensionality of the sample (or mapped kernel feature) space, and 2) high computational complexity. In this chapter, we introduce a new kernel discriminant learning method, which attempts to deal with the two problems by using regularization and subspace decomposition techniques. The proposed method is tested by extensive experiments performed on real face databases. The obtained results indicate that the method outperforms, in terms of classification accuracy, existing kernel methods, such as kernel Principal Component Analysis and kernel Linear Discriminant Analysis, at a significantly reduced computational cost.

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Using discriminant eigenfeatures for image retrieval

Cited by 1,436 publications

References 16 publications

Optimized Data Preprocessing for Multivariate Analysis Applied to ^99mTc-ECD SPECT Data Sets of Alzheimer's Patients and Asymptomatic Controls

Optimized Data Preprocessing for Multivariate Analysis Applied to ^99mTc-ECD SPECT Data Sets of Alzheimer's Patients and Asymptomatic Controls

Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition

Kernel Discriminant Learning with Application to Face Recognition

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Using discriminant eigenfeatures for image retrieval

Cited by 1,436 publications

References 16 publications

Optimized Data Preprocessing for Multivariate Analysis Applied to 99mTc-ECD SPECT Data Sets of Alzheimer's Patients and Asymptomatic Controls

Optimized Data Preprocessing for Multivariate Analysis Applied to 99mTc-ECD SPECT Data Sets of Alzheimer's Patients and Asymptomatic Controls

Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition

Kernel Discriminant Learning with Application to Face Recognition

Contact Info

Product

Resources

About

Optimized Data Preprocessing for Multivariate Analysis Applied to ^99mTc-ECD SPECT Data Sets of Alzheimer's Patients and Asymptomatic Controls

Optimized Data Preprocessing for Multivariate Analysis Applied to ^99mTc-ECD SPECT Data Sets of Alzheimer's Patients and Asymptomatic Controls