Adapted B-CUBED Metrics to Unbalanced Datasets

Moreno, José G.; Dias, Gaël

doi:10.1145/2766462.2767836

Cited by 3 publications

(2 citation statements)

References 7 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…F P and F B are two widely used metrics to evaluate clustering performance. Basically, the performance of F P and F B is dominated by the large-size clusters [2,21]. Note that we omit the metric of NMI in prior works [31,35,36,38,39,41] due to its tendency to choose the results with large number of clusters [1].…”

Section: Metrics For Face Clusteringmentioning

confidence: 99%

“…In addition, performance of face clustering methods is usually evaluated based on Pairwise F-score (F P ) [32] and BCubed F-score (F B ) [2]. The two traditional metrics are biased toward large-size clusters [2,21], which grossly neglect the negative impact of incorrect partitions on small-size clusters. Those clusters create lots of burdens for subsequent applications because they misinformed the true number of clusters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

FaceMap: Towards Unsupervised Face Clustering via Map Equation

Yu¹,

Yang²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

Face clustering is an essential task in computer vision due to the explosion of related applications such as augmented reality or photo album management. The main challenge of this task lies in the imperfectness of similarities among image feature representations. Given an existing feature extraction model, it is still an unresolved problem that how can the inherent characteristics of similarities of unlabelled images be leveraged to improve the clustering performance. Motivated by answering the question, we develop an effective unsupervised method, named as FaceMap, by formulating face clustering as a process of non-overlapping community detection, and minimizing the entropy of information flows on a network of images. The entropy is denoted by the map equation and its minimum represents the least description of paths among images in expectation. Inspired by observations on the ranked transition probabilities in the affinity graph constructed from facial images, we develop an outlier detection strategy to adaptively adjust transition probabilities among images. Experiments with ablation studies demonstrate that FaceMap significantly outperforms existing methods and achieves new state-of-the-arts on three popular large-scale datasets for face clustering, e.g., an absolute improvement of more than 10% and 4% comparing with prior unsupervised and supervised methods respectively in terms of average of Pairwise F-score. Our code is publicly available on github 2 .

show abstract

Section: Metrics For Face Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FaceMap: Towards Unsupervised Face Clustering via Map Equation

Yu¹,

Yang²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Bcubed revisited: elements like me

van Heusden,

Kamps,

Marx

2024

Discov Computing

View full text Add to dashboard Cite

BCubed is a mathematically clean, elegant and intuitively well behaved external performance metric for clustering tasks. BCubed compares a predicted clustering to a known ground truth clustering through elementwise precision and recall scores. For each element, the predicted and ground truth clusters containing the element are compared, and the mean over all elements is taken. We argue that BCubed overestimates performance, for the intuitive reason that the clustering gets credit for putting an element into its own cluster. This is repaired, and we investigate the repaired version, called “Elements Like Me (ELM)”. We extensively evaluate ELM from both a theoretical and empirical perspective, and conclude that it retains all of its positive properties, and yields a minimum zero score when it should. Synthetic experiments show that ELM can produce different rankings of predicted clusterings when compared to BCubed, and that the ELM scores are distributed with lower mean and a larger variance than BCubed.

show abstract

Multimodal Web Page Segmentation Using Self-organized Multi-objective Clustering

Ramesh

Dias

Andrew

et al. 2022

ACM Trans. Inf. Syst.

View full text Add to dashboard Cite

Web page segmentation (WPS) aims to break a web page into different segments with coherent intra- and inter-semantics. By evidencing the morpho-dispositional semantics of a web page, WPS has traditionally been used to demarcate informative from non-informative content, but it has also evidenced its key role within the context of non-linear access to web information for visually impaired people. For that purpose, a great deal of ad hoc solutions have been proposed that rely on visual, logical, and/or text cues. However, such methodologies highly depend on manually tuned heuristics and are parameter-dependent. To overcome these drawbacks, principled frameworks have been proposed that provide the theoretical bases to achieve optimal solutions. However, existing methodologies only combine few discriminant features and do not define strategies to automatically select the optimal number of segments. In this article, we present a multi-objective clustering technique called MCS that relies on \( K \) -means, in which (1) visual, logical, and text cues are all combined in a early fusion manner and (2) an evolutionary process automatically discovers the optimal number of clusters (segments) as well as the correct positioning of seeds. As such, our proposal is parameter-free, combines many different modalities, does not depend on manually tuned heuristics, and can be run on any web page without any constraint. An exhaustive evaluation over two different tasks, where (1) the number of segments must be discovered or (2) the number of clusters is fixed with respect to the task at hand, shows that MCS drastically improves over most competitive and up-to-date algorithms for a wide variety of external and internal validation indices. In particular, results clearly evidence the impact of the visual and logical modalities towards segmentation performance.

show abstract

Adapted B-CUBED Metrics to Unbalanced Datasets

Cited by 3 publications

References 7 publications

FaceMap: Towards Unsupervised Face Clustering via Map Equation

FaceMap: Towards Unsupervised Face Clustering via Map Equation

Bcubed revisited: elements like me

Multimodal Web Page Segmentation Using Self-organized Multi-objective Clustering

Contact Info

Product

Resources

About