This example shows how to create a variational autoencoder (VAE) in MATLAB to generate digit images. VAEs differ from regular autoencoders in that they do not use the encoding-decoding process to reconstruct an input. Using Autoencoders for Image Classification . So what pre processing should i do to the colour images since colour images are matrix in 3 dimensions, for the stacked autoencoders to work. As a result, an accuracy of 99.16% was achieved. But for colour images, it has 3 colour channels, RGB. It needs to be NxD where N is the number of samples (30 in this case) and D is feature dimension. By Radhesyam Gudipudi . This data set is one of the most widely used data sets for testing new image classification models. These features were obtained from the image data processed by the AutoEncoder network. Finally, the image clustering is carried out by K-means++ algorithm. feature values are obtained by the Multi-autoencoder. If you are using raw images as features you need to reshape those from 100x100 to 1x10000 before using svmtrain. Image classification using Autoencoders – MATLAB Training a deep neural network to classify images of hand-written digits from the MNIST dataset. How Autoencoders Enable AI to Classify Images . To load the data from the files as MATLAB arrays, ... which are used in the example Train Variational Autoencoder (VAE) to Generate Images. Feature extraction using Image processing and Multi-autoencoder The image dataset used in this paper is caltech1015 that is a set of color natural images (32 H32 pixel) such as watch, motorbike, airplane, grand piano, etc. You convert the image matrix to an array, rescale it between 0 and 1, reshape it so that it's of size 28 x 28 x 1, and feed this as an input to the network. The VAE generates hand-drawn digits in the style of the MNIST data set. - H2K804/digit-classification-autoencoder My guess is that you aren't resizing the training data correctly. Machine learning tasks are usually described in terms of how the machine learning model should process given data. The SVM model ensured 99.28% classification accuracy using this feature set. Image Classification Using the Variational Autoencoder. With our described method of using embedding images with a trained encoder (extracted from an autoencoder), we provide here a simple concrete example of how we can query and retrieve similar images in a database. matlab image-processing supervised-learning pca image-classification image-recognition support-vector-machine image-segmentation svm-training matlab-image-processing-toolbox k-means-clustering Updated Aug 16, 2018 In my case (using the Variational Autoencoder to separate Football Images from ads), I had to break videos into frames (images). 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