Image Classification Using the Variational Autoencoder. 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. 2.1. It needs to be NxD where N is the number of samples (30 in this case) and D is feature dimension. The SVM model ensured 99.28% classification accuracy using this feature set. - H2K804/digit-classification-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. The images are of size 28 x 28 x 1 or a 30976-dimensional vector. This example shows how to create a variational autoencoder (VAE) in MATLAB to generate digit images. 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 . This data set is one of the most widely used data sets for testing new image classification models. If you are using raw images as features you need to reshape those from 100x100 to 1x10000 before using svmtrain. The example given on matlab site for image classification of MNIST dataset is only for black and white images which has only one colour channel. 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 As mentioned earlier, the code for our similar image recommender system can be found at: By Radhesyam Gudipudi . In the fourth process, the most relevant 1000 features provided by the RR were taken into account. VAEs differ from regular autoencoders in that they do not use the encoding-decoding process to reconstruct an input. To load the data from the files as MATLAB arrays, ... which are used in the example Train Variational Autoencoder (VAE) to Generate Images. But for colour images, it has 3 colour channels, RGB. Machine learning tasks are usually described in terms of how the machine learning model should process given data. 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. feature values are obtained by the Multi-autoencoder. Finally, the image clustering is carried out by K-means++ algorithm. As a result, an accuracy of 99.16% was achieved. The VAE generates hand-drawn digits in the style of the MNIST data set. My guess is that you aren't resizing the training data correctly. 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. 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