The literature on cluster analysis methods evaluating the contribution of features to the emergence of the cluster structure for a given clustering partition is sparse. Despite advances in explainable supervised methods, explaining the outcomes of unsupervised algorithms is a less explored area. This paper proposes two post-hoc algorithms to determine feature importance for prototype-based clustering methods. The first approach assumes that the variation in the distance among cluster prototypes after marginalizing a feature can be used as a proxy for feature importance. The second approach, inspired by cooperative game theory, determines the contribution of each feature to the cluster structure by analyzing all possible feature coalitions. Multiple experiments using real-world datasets confirm the effectiveness of the proposed methods for both hard and fuzzy clustering settings.

License Plate Recognition (LPR) plays a critical role in various applications, such as toll collection, parking management, and traffic law enforcement. Although LPR has witnessed significant advancements through the development of deep learning, there has been a noticeable lack of studies exploring the potential improvements in results by fusing the outputs from multiple recognition models. This research aims to fill this gap by investigating the combination of up to 12 different models using straightforward approaches, such as selecting the most confident prediction and employing majority vote-based strategies. Our experiments encompass a wide range of datasets, revealing substantial benefits of fusion approaches in both intra- and cross-dataset setups. Essentially, fusing multiple models reduces considerably the likelihood of obtaining subpar performance on a particular dataset/scenario. We also found that combining models based on their speed is a compelling approach. Specifically, for applications where the recognition task can tolerate some additional time, though not excessively, an effective strategy is to combine 4–6 fast models. These models may not be the most accurate individually, but their fusion strikes an optimal balance between accuracy and speed.

We study the problem of predicting hierarchical image segmentations using supervised deep learning.

While deep learning methods are now widely used as contour detectors, the lack of image datasets with hierarchical annotations has prevented researchers from explicitly training models to predict hierarchical contours.

Image segmentation has been widely studied, but it is limited by only proposing a segmentation at a single scale.

Hierarchical image segmentation solves this problem by proposing segmentation at multiple scales, capturing objects and structures at different levels of detail.

However, this area of research appears to be less underexplored and therefore no hierarchical image segmentation dataset exists.

In this paper, we provide a hierarchical adaptation of the Pascal-Part dataset, and use it to train a neural network for hierarchical image segmentation prediction.

We demonstrate the efficiency of the proposed method through three benchmarks: the precision-recall and F-score benchmarks for boundary location, the level recovery fraction for assessing hierarchy quality, and the false discovery fraction. We show that our method successfully learns hierarchical boundaries in the correct order, and achieves better performance than the state-of-the-art model trained on single-scale segmentations.

Remote teaching has been used successfully with the evolution of videoconference solutions and broadband internet availability. Even several years before the global COVID 19 pandemic, Ceibal used this approach for different educational programs in Uruguay. As in face-to-face lessons, teaching evaluation is a relevant task in this context, which requires many time and human resources for classroom observation. In this work we propose automatic tools for the analysis of teaching practices, taking advantage of the lessons recordings provided by the videoconference system. We show that it is possible to detect with a high level of accuracy, relevant lessons metrics for the analysis, such as the teacher talking time or the language usage in English lessons.