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CNN classifier using 1D, 2D and 3D feature vectors

versión 1.0.4 (340 KB) por Selva
using CNN network with pre-extracted feature vectors instead of automatically deriving the features by itself from image.

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Actualizada 16 May 2019

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CNN deep network consist of inbuilt feature extraction (flattening) layer along with classification layers. By omitting the feature extraction layer (conv layer, Relu layer, pooling layer), we can give features such as GLCM, LBP, MFCC, etc directly to CNN just to classify alone. This can be acheived by building the CNN architecture using fully connected layers alone. This is helpful for classifying audio data.

http://cs231n.github.io/convolutional-networks/ visit this page for doubts regarding the architecture. I have used C->R->F->F->F architecture

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Selva (2022). CNN classifier using 1D, 2D and 3D feature vectors (https://www.mathworks.com/matlabcentral/fileexchange/68882-cnn-classifier-using-1d-2d-and-3d-feature-vectors), MATLAB Central File Exchange. Recuperado .

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Se creó con R2017b
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