Abstract
Video summarization is an important tool considering the amount of data to analyze. Techniques in this area aim to yield synthetic and useful visual abstraction of the videos contents. Hence, in this paper we present a new summarization algorithm, based on image features, which is composed by the following steps: (i) Query video processing using cosine similarity metric and total variation smoothing to identify classes in the query sequence; (ii) With this result, build a labeled training set of frames; (iii) Generate the unlabeled training set composed by samples of the video database; (iv) Training a deep semi-supervised autoencoder; (v) Compute the K-means for each video separately, in the encoder space, with the number of clusters set as a percentage of the video size; (vi) Select key-frames in the K-means clusters to define the summaries. In this methodology, the query video is used to incorporate prior knowledge in the whole process through the obtained labeled data. The step (iii) aims to include unknown patterns useful for the summarization process. We evaluate the methodology using some videos from OPV video database. We compare the performance of our algorithm with the VSum. The results indicate that the pipeline was well succeed in the summarization presenting a F-score value superior to VSum.

Abstract

Abstract
When encountering a dubious diagnostic case, radiologists typically search in public or internal databases for similar cases that would help them in their decision-making process. This search represents a massive burden to their workflow, as it considerably reduces their time to diagnose new cases. It is, therefore, of utter importance to replace this manual intensive search with an automatic content-based image retrieval system. However, general content-based image retrieval systems are often not helpful in the context of medical imaging since they do not consider the fact that relevant information in medical images is typically spatially constricted. In this work, we explore the use of interpretability methods to localize relevant regions of images, leading to more focused feature representations, and, therefore, to improved medical image retrieval. As a proof-of-concept, experiments were conducted using a publicly available Chest X-ray dataset, with results showing that the proposed interpretability-guided image retrieval translates better the similarity measure of an experienced radiologist than state-of-the-art image retrieval methods. Furthermore, it also improves the class-consistency of top retrieved results, and enhances the interpretability of the whole system, by accompanying the retrieval with visual explanations. © Springer Nature Switzerland AG 2020.

Abstract
Fingerprint presentation attack detection (PAD) methods present a stunning performance in current literature. However, the fingerprint PAD generalisation problem is still an open challenge requiring the development of methods able to cope with sophisticated and unseen attacks as our eventual intruders become more capable. This work addresses this problem by applying a regularisation technique based on an adversarial training and representation learning specifically designed to to improve the PAD generalisation capacity of the model to an unseen attack. In the adopted approach, the model jointly learns the representation and the classifier from the data, while explicitly imposing invariance in the high-level representations regarding the type of attacks for a robust PAD. The application of the adversarial training methodology is evaluated in two different scenarios: i) a handcrafted feature extraction method combined with a Multilayer Perceptron (MLP); and ii) an end-to-end solution using a Convolutional Neural Network (CNN). The experimental results demonstrated that the adopted regularisation strategies equipped the neural networks with increased PAD robustness. The adversarial approach particularly improved the CNN models' capacity for attacks detection in the unseen-attack scenario, showing remarkable improved APCER error rates when compared to state-of-the-art methods in similar conditions.

Abstract

Abstract