Feature Fusion Method Based on KCCA for Ear and Profile Face Based Multimodal Recognition

Xu, Xiaona; Zhang, Mu

doi:10.1109/ical.2007.4338638

Cited by 45 publications

(19 citation statements)

References 6 publications

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“…At last canonical variables can be (6)~(7).Also ρ can be work out.More detail could be found in [7,8].…”

Section: A Kcca Theorymentioning

confidence: 98%

“…In multivariate statistical analysis,it usually needs to study about the relevant problems of two sets of variables.You can translate the correlation between two sets of variables into the correlation between fewer pairs of variables from linear combination of original variables.This idea was developed by Hotelling in 1936,which was called Canonical Correlation Analysis (CCA) [7].Recently CCA has been applied to a lot of …”

Section: A Canonical Correlation Analysis Theorymentioning

confidence: 99%

“…Sun proposed a new feature fusion method based on CCA, improved face recognization rate [2].Then Xu and zhao introduced kernel method based on Sun's method, achieved better result than Sun's [7,8]. Based on the previous researches,this paper proposes a novel framework for measuring and fusing two feature sets in steganographic detection field, it aims at analysing the relation between basic features for feature selection, obtaining more discriminating classification information for better detection result and reducing feature dimension.…”

Section: Introductionmentioning

confidence: 99%

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KCCA feature fusion in universal steganographic detection

Zhong

2011

2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC)

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Feature fusion method has improved steganographic detection performance based on classical feature, however there are some drawbacks of this: without analysing the correlation of the basic features,it's only a simple combination of features and lacks standard for features selection; serial fusion feature always has high dimension,which will lead great time cost and possibility of "curse of dimensionality".In this paper,we proposed a novel framework for measuring the feature selection and fusing two selected feature sets in steganographic detection field, based on KCCA theory. KCCA feature fusion method can outperform single feature and achieve similar performance to serial feature fusion method in steganographic detection field,while only costing 1/10~1/8 of original time. So it has better practicability.

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“…At last canonical variables can be (6)~(7).Also ρ can be work out.More detail could be found in [7,8].…”

Section: A Kcca Theorymentioning

confidence: 98%

Section: A Canonical Correlation Analysis Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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KCCA feature fusion in universal steganographic detection

Zhong

2011

2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC)

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“…For each layer's features of HP sub-band images extracted by WTA-ICA model, the weighted average feature fusion technique was first utilized here, and the fusion features between the first layer's and the second layer's HP sub-band image features was denoted by A , the fusion feature method based on kernel canonical correlation analysis (KCCA) [9] was discussed again. Thus, the total fusion features total A of test images were obtained.…”

Section: Image Reconstruction Based On Fusion Featuresmentioning

confidence: 99%

Image Reconstruction Using WTA-ICA Model in Contourlet Transform Domain

Shang¹,

Sun²

2014

Proceedings of the 3rd International Conference on Computer Science and Service System

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Abstract-A new image reconstruction method using the WTA-ICA model in contourlet transform domain is discussed in this paper. WTA-ICA is in fact an sparse ICA algorithm, and is simpler and faster under high dimensional computational requirements. Contourlet transform can offer a flexible multiresolution and directional decomposition for images and embody well image structure. Here, for a given image, the contourlet transform is first used to obtain low and high frequency sub-band images at two layers, where each layer behaves four directions. Then, WTA-ICA model is used in contourlet domain to extract low frequency sub-band image features and each layer's high frequency sub-band image features. Further, considered the fusion technique between each layer's high frequency sub-band features, as well as that between the fused high frequency sub-band image features and the first level's low frequency features, the total image fused features can be obtained. Using this fusion feature, an image can be reconstructed well. The quality of reconstructed images is measured by SNR criterion, and compared with WTA-ICA model, the experimental results shown that our method is indeed efficient in image reconstruction task.

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“…Given one complete feature set, several research work applied Canonical Correlation Analysis (CCA) to model the correlations between features [124,169,202]. Sargin et al [169] apply CCA to fuse audio and lip texture features to achieve audiovisual synchronization.…”

Section: Early Fusion or Late Fusionmentioning

confidence: 99%

Integrating Deep Learning with Correlation-based Multimedia Semantic Concept Detection

Ha¹

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Feature Fusion Method Based on KCCA for Ear and Profile Face Based Multimodal Recognition

Cited by 45 publications

References 6 publications

KCCA feature fusion in universal steganographic detection

KCCA feature fusion in universal steganographic detection

Image Reconstruction Using WTA-ICA Model in Contourlet Transform Domain

Integrating Deep Learning with Correlation-based Multimedia Semantic Concept Detection

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