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Latent fingerprint segmentation is a complex process of separating relevant areas called fingerprints from an irrelevant background in the latent fingerprint image which is of poor quality. A breakthrough in the field can be used to segment fingerprints accurately from the background by using optimal resources. Processing of unwanted background of the entire image can lead to false and missed detection of fingerprints. An early fingerprint distinction technique based on colour and saliency masks is proposed to detect potentially relevant areas out of the entire image area for further processing, using a non-learning approach. Later, the patches of early detected fingermarks are fed to a stacked convolutional autoencoder for separating imposters of fingerprint(s) region from relevant fingerprint(s) regions, using a deep learning approach. The inspiration to use the convolutional neural network in this hybrid approach is to effectively capture feature distinction from potential features ....
Graph representation learning aims at mapping a graph into a lower-dimensional feature space. Deep attributed graph representation, utilizing deep learning models on the graph structure and attributes, shows its significance in mining complex relational data. Most existing deep attributed graph representation models assume graph attributes in a single-attributed view. However, rich information in real-world applications demands the ability to handle multiple attributed views. For example, in social network users' profiles and posts represent two distinct attributed views. A single-attributed view or a simple ensemble of them fails to represent the rich information and complex relations therein. To confront this challenge, this paper proposes a novel deep unsupervised graph representation learning model, called Multi-attributed-view graph Convolutional AutoEncoder (MagCAE). MagCAE learns the node-pairwise proximity among multi-attributed views and node embeddings, across which a no ....