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