Abstract Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority class. Cost-sensitive learning tackles this problem by treating the classes differently, formulated typically via a user-defined fixed misclassification cost matrix provided as input to the learner. Such parameter tuning is a challenging task that requires domain knowledge and moreover, wrong adjustments might lead to overall predictive performance deterioration. In this work, we propose a novel cost-sensitive boosting approach for imbalanced data that dynamically adjusts the misclassification costs over the boosting rounds in response to model’s performance instead of using a fixed misclassification cost matrix. Our method, called AdaCC, is parameter-free as it relies on the cumulative behavior of the boosting model in order to adjust the misclassification costs for the next boosting round and comes with theoretical guarantees regarding the training error. Experiments on 27 real-world datasets from different domains with high class imbalance demonstrate the superiority of our method over 12 state-of-the-art cost-sensitive boosting approaches exhibiting consistent improvements in different measures, for instance, in the range of [0.3–28.56%] for AUC, [3.4–21.4%] for balanced accuracy, [4.8–45%] for gmean and [7.4–85.5%] for recall.

In this paper, we present Constrained Mean Shift (CMS), a novel approach for mean shift clustering under sparse supervision using cannot-link constraints. The constraints provide a guidance in constrained clustering indicating that the respective pair should not be assigned to the same cluster. Our method introduces a density-based integration of the constraints to generate individual distributions of the sampling points per cluster. We also alleviate the (in general very sensitive) mean shift bandwidth parameter by proposing an adaptive bandwidth adjustment which is especially useful for clustering imbalanced data sets. Several experiments show that our approach achieves better performance compared to state-of-the-art methods both clustering synthetic data sets as well as clustering encoded features of real-world image data sets.

Roads constitute a major part of the lives of everybody. Heavy use, for instance by cars and especially trucks, and even soil movement lead to visible damages. While major roads are regularly inspected, smaller roads often lack attention. It is therefore of great interest to have camera-based systems which can automatically detect and even classify damages.This report presents a system developed by the authors as part of the Road Damage Detection and Classification Challenge at the 2018 IEEE Big Data Cup [1]. Further contributions made here are techniques to augment the small set of training data. As a major contribution we also propose refinements to the dataset and evaluation metric to improve the challenge.

Modern video coding standards like High Efficiency Video Coding (HEVC) provide superior coding efficiency. However, this does not state true for complex and hard to predict textures which require high bit rates to achieve a high quality. To overcome this limitation of HEVC, texture synthesis frameworks were proposed in previous works. However, these frameworks only result in good reconstruction quality if the decomposition into synthesizable and non-synthesizable regions is either known or trivial. The frameworks fail for more challenging content, e.g. for content with fine non-synthesizable details within synthesizable regions. To enable texture synthesis-based video coding with high quality for this content, we propose sophisticated detail-aware decomposition techniques in this paper. These techniques are based on an initial coarse segmentation step followed by a refinement step that detects even small differences in the previously segmented region. With this new approach, we are able to achieve average luma BD-rate gains of 13.77% over HEVC and 3.03% over the closest related work from the literature. Furthermore, the considerably improved visual quality in addition to the bit rate savings is confirmed by comprehensive subjective tests.