[1]REN Yan,ZHANG Qian.A facial semantic extraction method based on axiomatic fuzzy sets and granular computing[J].CAAI Transactions on Intelligent Systems,2022,17(5):1021-1031.[doi:10.11992/tis.202108023]
Copy

A facial semantic extraction method based on axiomatic fuzzy sets and granular computing

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 5 Page number: 1021-1031 Column: 学术论文—人工智能基础 Public date: 2022-09-05
Title:
A facial semantic extraction method based on axiomatic fuzzy sets and granular computing
Author(s):
REN Yan1;  ZHANG Qian2
1. College of Artificial Intelligence, Shenyang Aerospace University, Shenyang 110136, China;
2. College of Automation, Shenyang Aerospace University, Shenyang 110136, China
Keywords:
semantic extraction; AFS theory; information granules; clustering; face retrieval; critical point detection; membership function; axiomatic fuzzy sets
CLC:
TP391.4
DOI:
10.11992/tis.202108023
Abstract:
People prefer to distinguish the similarity of faces in the field of face retrieval and verification by describing the “semantics” or “concept” of object features, which cannot be satisfied by traditional image retrieval technology. Therefore, this paper presents a facial semantic extraction algorithm (IAFSGD) that is based on axiomatic fuzzy sets (AFS) and information granules. First, the face images are corrected to detect the critical points where facial features are extracted; then, the samples of facial features are clustered to construct the class center; the information granules of each class are obtained under the AFS framework; and then the facial images are reclassified through the information granules, to obtain the final clustering results and interpretable facial semantic description; finally, the efficacy of this algorithm is demonstrated on such facial datasets as Multi-PIE, AR, and FEI. Experimental results show that compared with FCM, CAN, FCMGD, AFSGD, and KTM, the semantic extraction method proposed in this paper can obtain clustering results closer to human perception and that the results are of good interpretability.
References:
[1] REN Y, LI Q, LIU W, et al. Semantic facial descriptor extraction via Axiomatic Fuzzy Set[J]. Neurocomputing, 2016, 171: 1462–1474.
[2] PALACIOS A, PALACIOS J, SANCHEZ L, et al. Genetic learning of the membership functions for mining fuzzy association rules from low quality data[J]. Information sciences, 2015, 295: 358–378.
[3] KARCZMAREK P, KIERSZTYN A, RUTKA P, et al. Linguistic descriptors in face recognition: a literature survey and the perspectives of future development[C]// 2015 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). [S.l.]. IEEE, 2015: 98?103.
[4] LIU Xiaodong. A new mathematical axiomatic system of fuzzy sets and systems[J]. Journal of Dalian Maritime University, 1995, 3: 559–560.
[5] CAI Y, ZHANG H, SUN S, et al. Axiomatic fuzzy set theory-based fuzzy oblique decision tree with dynamic mining fuzzy rules[J]. Neural computing and applications, 2020, 32(15): 11621–11636.
[6] LI Zedong, DUAN Xiaodong, ZHANG Qingling, et al. Multi-ethnic facial features extraction based on axiomatic fuzzy set theory[J]. Neurocomputing, 2017, 242: 161–177.
[7] WANG Weina, LIU Xiaodong. Fuzzy forecasting based on automatic clustering and axiomatic fuzzy set classification[J]. Information sciences, 2015, 294: 78–94.
[8] LIU Mengmeng, LIU Xiaodong, JIA Wenjuan, et al. The trading strategy of inflection point futures analysis based on AFS theory[C]//2020 39th Chinese Control Conference (CCC). Shenyang, China. IEEE, 2020: 2170?2175.
[9] 储德润, 周治平. 公理化模糊共享近邻自适应谱聚类算法[J]. 智能系统学报, 2019, 14(5): 897–904
CHU Derun, ZHOU Zhiping. Shared nearest neighbor adaptive spectral clustering algorithm based on axiomatic fuzzy set theory[J]. CAAI transactions on intelligent systems, 2019, 14(5): 897–904
[10] LIU X, PEDRYCZ W. Axiomatic fuzzy set theory and its applications[M]. Berlin, Heidelberg: Springer, 2009.
[11] DUAN Xiaodong, WANG Yuangang, PEDRYCZ W, et al. AFSNN: a classification algorithm using axiomatic fuzzy sets and neural networks[J]. IEEE transactions on fuzzy systems, 2018, 26(5): 3151–3163.
[12] JIA W, LIU X, WANG Y, et al. Semisupervised learning via axiomatic fuzzy set theory and SVM[J]. IEEE transactions on cybernetics, 2020(99): 1–14.
[13] LIANG A, LIU W, LI L, et al. Accurate facial landmarks detection for frontal faces with extended tree-structured models[J]. IEEE, 2014: 538–543.
[14] LIU Xiaodong, REN Yan. Novel artificial intelligent techniques via AFS theory: feature selection, concept categorization and characteristic description[J]. Applied soft computing, 2010, 10(3): 793–805.
[15] PEDRYCZ W, SUCCI G, SILLITTI A, et al. Data description: a general framework of information granules[J]. Knowledge-based Systems, 2015, 80: 98–108.
[16] 刘翠君, 赵才荣, 苗夺谦, 等. 粒化的Mean Shift行人跟踪算法[J]. 智能系统学报, 2016, 11(4): 433–441
LIU Cuijun, ZHAO Cairong, MIAO Duoqian, et al. Granular mean shift pedestrian tracking algorithm[J]. CAAI transactions on intelligent systems, 2016, 11(4): 433–441
[17] GROSS R, MATTHEWS I, COHN J, et al. Multi-PIE[J]. Image and vision computing, 2010, 28(5): 807–813.
[18] MARTINEZ A, BENAVENTE R. The AR face database[R]. Computer Vision Center Technical Report, 1998, 24.
[19] LIN S, CHEN W, LIN C, et al. Building a Chinese facial expression database for automatically detecting academic emotions to support instruction in blended and digital learning environments[J]. Innovative technologies and learning, 2019, 11937: 155–162.
[20] PAL N R, PAL K, KELLER J M, et al. A possibilistic fuzzy c-Means clustering algorithm[J]. IEEE transactions on fuzzy systems, 2005, 13(4): 517–530.
[21] NIE F, WANG X, HUANG H. Clustering and projected clustering with adaptive neighbors[M]. New York: ACM, 2014.
[22] REN Y, GUAN W, LIU W, et al. Facial semantic descriptors based on information granules[J]. Information sciences, 2019, 479: 335–354.
[23] REN Y, ZHANG S, LIU W, et al. An advisable facial semantic characterization based on Axiomatic Fuzzy Set theory and information granules[J]. Information sciences, 2020, 523: 133–151.
[24] TAVALLALI P, TAVALLALI P, SINGHAL M. K-means tree: an optimal clustering tree for unsupervised learning[J]. The journal of supercomputing, 2021, 77(5): 5239–5266.
[25] HUBERT L, ARABIE P. Comparing partitions[J]. Journal of classification, 1985, 2(1): 193–218.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems