Titlebook: Structural, Syntactic, and Statistical Pattern Recognition; Joint IAPR Internati Adam Krzyzak,Ching Y. Suen,Nicola Nobile Conference procee

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,Graph Regression Based on?Graph Autoencoders,ature vectors through a Graph Autoencoder (G-AE). Then, derived vectors are used to perform a prediction task using both a Neural Network (NN) and a regressor separately. Results for graph property prediction of both models compared to one another and baselined against a classical graph regression t

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,A Capsule Network for?Hierarchical Multi-label Image Classification,lassification problem is arranged into smaller ones based upon a hierarchy or taxonomy. Thus, hierarchical classification modes generally provide multiple class predictions on each instance, whereby these are expected to reflect the structure of image classes as related to one another. In this paper

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,Monte Carlo Dropout for?Uncertainty Analysis and?ECG Trace Image Classification,y cardiologists to indicate any unexpected cardiac activity. Deep Neural Networks (DNN) serve as a highly successful method for classifying ECG images for the purpose of computer-aided diagnosis. However, DNNs can not quantify uncertainty in predictions, as they are incapable of discriminating betwe

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One-Against-All Halfplane Dichotomies,o solve M arbitrary linear dichotomies with approximately equal class memberships. An explanation of the rapid growth with M and N of the number of separable one-against-all linear halfplane dichotomies is proposed in terms of convex polyhedra in a hyperspherical shell. The counterintuitive surge is

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,Discovering Respects for?Visual Similarity,nerally compared and .. As an indirect evaluation, we propose generating an automatic explanation of clusters of image pairs found using a model pre-trained on texts/images. An experimental study highlights encouraging results toward a better comprehension of visual similarity.

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0302-9743 , S+SSPR 2022, held in Montreal, QC, Canada, in August 2022..The 30 papers together with 2 invited talks presented in this volume were carefully reviewed and selected from 50 submissions. The workshops presents papers on topics such as deep learning, processing, computer vision, machine learning and