Semantics characterization for eye shapes based on directional triangle-area curve clustering

Ren, Yan; Li, Qilin; Liu, Wanquan; Li, Ling; Guan, Wei

doi:10.1007/s11042-019-7659-4

Cited by 4 publications

(2 citation statements)

References 49 publications

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“…Reference [7] develop an eyebrow semantic description via clustering based on Axiomatic Fuzzy Set. References [8] and [9] propose the semantics extraction algorithms to obtain the shape characterizations for eyes and eyebrows, respectively. The facial semantic descriptor in [10] is proposed based on information granules to represent the facial semantics, the results shows that this descriptor not only can characterize the key semantics of facial components of data, but also can improve the semantic classification performance in comparison with human perception.…”

Section: Introductionmentioning

confidence: 99%

Notice of Retraction: Interpretable Facial Semantic Extractor Based on Axiomatic Fuzzy Set

Ren

Zhang

et al. 2019

IEEE Access

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The main purpose of this paper is to label one face to which it refers using a commonly accepted concept or linguistic term that corresponds to a given face class. Therefore, a facial semantic description method based on the axiomatic fuzzy set (AFS) theory is proposed. Firstly, the facial geometric features are defined based on the landmarks, such as distance features, angle features and scale features, etc. And then the salient features are selected via AFS feature selection method, and the fuzzy linguistic terms are defined the selected features. Thirdly, the facial descriptions which can characterize the salient characteristics are extracted via a clustering scheme in the framework of AFS theory. Multiple experiments are done on Chinese ethnic face database including Korean, Uyghur and Zhuang. The experimental results demonstrate that AFS feature selection can obtain higher accuracies via AFS clustering algorithm for each ethnic group than that via mRMR. Furthermore, compared with k-means, FCM and Minmax k-means, AFS clustering algorithm obtains the highest accuracies for each ethnic group, which verifies the efficacy of the proposed semantic extractor. Consequently, one comparative experimental results verify the consistency between the extracted semantic description method and the facial physical characteristics in physical anthropology. Also the facial semantics would be very useful to further describe some facial action units or facial expressions, which can be further used for affection analysis of images as well as semi-automatic action unit/facial expression annotation.

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Section: Introductionmentioning

confidence: 99%

Notice of Retraction: Interpretable Facial Semantic Extractor Based on Axiomatic Fuzzy Set

Ren

Zhang

et al. 2019

IEEE Access

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“…e corresponding algorithms mainly include the construction of similarity structure, accurate extraction of semantic content of data information, and similarity calculation. e algorithm has a certain application value, which solves the problem of the traditional semantic similarity algorithm to a certain extent, but there is still the problem of algorithm loss when the amount of data information is large [16,17]; based on the structure of text information, relevant researchers propose a corresponding distance algorithm. is algorithm first calculates the corresponding distance length between the corresponding texts and identifies the corresponding ontology model between the two nodes with the farther 2 Complexity distance, the smaller the similarity.…”

Section: Related Work Analysis: Analysis Of the Current Research Status Of Semantic Similarity Algorithmmentioning

confidence: 99%

Improvement and Analysis of Semantic Similarity Algorithm Based on Linguistic Concept Structure

2021

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With the rapid development of information age, various social groups and corresponding institutions are producing a large amount of information data every day. For such huge data storage and identification, in order to manage such data more efficiently and reasonably, traditional semantic similarity algorithm emerges. However, the accuracy of the traditional semantic similarity algorithm is relatively low, and the convergence of corresponding algorithm is poor. Based on this problem, this paper starts with the conceptual structure of language, analyzes the depth of language structure and the distance between nodes, and analyzes the two levels as the starting point. For the information of a specific data resource description frame type, the weight of interconnected edges is used for impact analysis so as to realize the semantic similarity impact analysis of all information data. Based on the above improvements, this paper also systematically establishes the data information modeling process based on language conceptual structure and establishes the corresponding model. In the experimental part, the improved algorithm is simulated and analyzed. The simulation results show that compared with the traditional algorithm, the algorithm has obvious accuracy improvement.

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An advisable facial semantic characterization based on Axiomatic Fuzzy Set theory and information granules

Ren

Zhang

Liu

et al. 2020

Information Sciences

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Semantics characterization for eye shapes based on directional triangle-area curve clustering

Cited by 4 publications

References 49 publications

Notice of Retraction: Interpretable Facial Semantic Extractor Based on Axiomatic Fuzzy Set

Notice of Retraction: Interpretable Facial Semantic Extractor Based on Axiomatic Fuzzy Set

Improvement and Analysis of Semantic Similarity Algorithm Based on Linguistic Concept Structure

An advisable facial semantic characterization based on Axiomatic Fuzzy Set theory and information granules

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