Titlebook: Computer Analysis of Images and Patterns; 19th International C Nicolas Tsapatsoulis,Andreas Panayides,Mario Vento Conference proceedings 20

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Land Use Change Detection Using Deep Siamese Neural Networks and Weakly Supervised Learning The architecture of the Siamese network is a combination of two multi-filter multi-scale deep convolutional neural networks (MFMS DCNN). Initially, the Siamese network is trained by utilizing the image-level semantic labels of the image pairs in the dataset. The features of the image pairs are obta

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AMI-Class: Towards a Fully Automated Multi-view Image Classifierwith a scalable AutoML library, DeepHyper. The proposed framework is able to, all at once, train a model to find a common latent representation and perform data imputation, choose the best classifier and tune all necessary hyper-parameters. Experiments on the MNIST data-set show the effectiveness of