3D Facial Similarity Measurement and Its Application in Facial Organization

Judging other people is a common and important task. Every day professionals make decisions that affect the lives of other people when they diagnose medical conditions, grant parole, or hire new employees. To prevent discrimination, professional standards require that decision makers render accurate and unbiased judgments solely based on relevant information. Facial similarity to previously encountered persons can be a potential source of bias. Psychological research suggests that people only rely on similarity-based judgment strategies if the provided information does not allow them to make accurate rule-based judgments. Our study shows, however, that facial similarity to previously encountered persons influences judgment even in situations in which relevant information is available for making accurate rule-based judgments and where similarity is irrelevant for the task and relying on similarity is detrimental. In two experiments in an employment context we show that applicants who looked similar to high-performing former employees were judged as more suitable than applicants who looked similar to low-performing former employees. This similarity effect was found despite the fact that the participants used the relevant résumé information about the applicants by following a rule-based judgment strategy. These findings suggest that similarity-based and rule-based processes simultaneously underlie human judgment.

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A Review on Correlation Maximized Similarity Measurement in Cross Media Retrieval Method

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i3.214218 ◽

2018 ◽

Vol 6 (3) ◽

pp. 214-218

Author(s):

Monelli Ayyavaraiah ◽

Keyword(s):

Similarity Measurement ◽

Retrieval Method ◽

Cross Media

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A Similarity Measurement with Entropy-Based Weighting for Clustering Mixed Numerical and Categorical Datasets

Algorithms ◽

10.3390/a14060184 ◽

2021 ◽

Vol 14 (6) ◽

pp. 184

Author(s):

Xia Que ◽

Siyuan Jiang ◽

Jiaoyun Yang ◽

Ning An

Keyword(s):

Categorical Data ◽

Clustering Algorithm ◽

Numerical Data ◽

Similarity Measurement ◽

Amount Of Information ◽

Automatic Categorization ◽

Categorical Attributes ◽

Weighting Strategy

Many mixed datasets with both numerical and categorical attributes have been collected in various fields, including medicine, biology, etc. Designing appropriate similarity measurements plays an important role in clustering these datasets. Many traditional measurements treat various attributes equally when measuring the similarity. However, different attributes may contribute differently as the amount of information they contained could vary a lot. In this paper, we propose a similarity measurement with entropy-based weighting for clustering mixed datasets. The numerical data are first transformed into categorical data by an automatic categorization technique. Then, an entropy-based weighting strategy is applied to denote the different importances of various attributes. We incorporate the proposed measurement into an iterative clustering algorithm, and extensive experiments show that this algorithm outperforms OCIL and K-Prototype methods with 2.13% and 4.28% improvements, respectively, in terms of accuracy on six mixed datasets from UCI.

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Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

Sensors ◽

10.3390/s21030696 ◽

2021 ◽

Vol 21 (3) ◽

pp. 696

Author(s):

Haipeng Chen ◽

Zeyu Xie ◽

Yongping Huang ◽

Di Gai

Keyword(s):

Brain Tumor ◽

Information Transfer ◽

Magnetic Resonance Images ◽

Low Noise ◽

Similarity Measurement ◽

Noise Sensitivity ◽

Fuzzy C Means ◽

Intuitionistic Fuzzy ◽

Membership Information ◽

Fuzzy C Means Algorithm

The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.

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