2019 International Conference on Wireless Networks and Mobile Communications (WINCOM) 2019
DOI: 10.1109/wincom47513.2019.8942469
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Fusion of Convolutional Neural Network and Statistical Features for Texture classification

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Cited by 6 publications
(2 citation statements)
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“…These features capture the rotation invariance of the ship images. JBene et al [ 21 ] discussed a method based on handcrafted and CNN features for texture classification. This work demonstrated that statistical features boost the classification performance of CNN when the deep features are concatenated with the handcrafted features—in that work, pretrained models, namely Xception and ResNet50 networks with handcrafted features LBP histogram, GLCM, Wavelet histogram, and Scale Invariant Feature transform-Fisher Vector (SIFT-FV) features for classification.…”
Section: Related Researchmentioning
confidence: 99%
“…These features capture the rotation invariance of the ship images. JBene et al [ 21 ] discussed a method based on handcrafted and CNN features for texture classification. This work demonstrated that statistical features boost the classification performance of CNN when the deep features are concatenated with the handcrafted features—in that work, pretrained models, namely Xception and ResNet50 networks with handcrafted features LBP histogram, GLCM, Wavelet histogram, and Scale Invariant Feature transform-Fisher Vector (SIFT-FV) features for classification.…”
Section: Related Researchmentioning
confidence: 99%
“…Mao et al[44] presented a method Deep residual pooling network obtained an accuracy of 85.72%. Jbene et al[49] Performed an analysis with Xception and SIFT-FV to obtain the performance of 86.10% on FMD. Experiments conducted demonstrate that the results in YIQ color model are excellent and promising.…”
mentioning
confidence: 99%