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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2020

19.05.2020

Multi-Featured and Fuzzy Based Dual Analysis Approach to Optimize the Subspace Clustering for Images

verfasst von: Kapil Juneja

Erschienen in: Wireless Personal Communications | Ausgabe 3/2020

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Abstract

In unsupervised classification, the subspace clustering is gaining the scope for the categorization of the more comprehensive and random image pool. In this paper, the visual and appearance features of images are evaluated independently and jointly for optimizing the subspace clustering. The normalized-image is divided into smaller blocks and extracted the visual and textural features. Entropy, Homogeneity, structural, and Edge content Features are evaluated for each block. The fuzzy rules are applied to the individual features for conducting the distinct block-adaptive hierarchical clustering. In the second level, the feature subspace is generated for exclusive features and applied to the hierarchical subspace clustering over it. After getting the cluster-segments for each image-feature and feature-subspace, the second-level fuzzy–rules are applied to assign the weights to each block. In the final stage, the image pool is processed based on this weighted poling and distance for identifying the image category. This collaborative evaluation based map performed the active clustering over the image pool. The proposed method is applied to AR, Extended-Yale, USPS, and Coil-20 Datasets. The comparative evaluation is conducted against Accuracy, NMI, and CE parameters. The proposed framework outperformed the SSC, LRR, LSR1, LSR2, SMR methods by 5.59%, 16.89%, 6.29%, 6.29%, 4.89% and 3.39% in NMI computation for AR dataset. The significant reduction in CE was achieved by 9.07%, 15.67%, 6.77%, 8.47%, 4.47% against SSE, LRR, LSR1, LSR2, and SMR methods for AR dataset. For the Extended Yale dataset, the proposed framework outperformed the existing clustering methods with 78.08% NMI and 21.11% CE. A significant higher NMI of 86.37% and least CE of 7.13% is achieved in this proposed model. For the Coil-20 dataset, the proposed model achieved 91.19% NMI and 82.83% accuracy, which is significantly better than existing methods.
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Literatur
1.
Taufik, A., Ahmad, S. S. S., & Khairuddin, N. F. E. (2017). Classification of landsat 8 satellite data using fuzzy c-means. International Conference on Machine Learning and Soft Computing, 1, 58–62.
2.
Lee, K.-C., Ho, J., & Kriegman, D. J. (2005). Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5), 684–698.
3.
Zhu, Y., Ting, K. M., & Carman, M. J. (2018). Grouping points by shared subspaces for effective subspace clustering. Pattern Recognition, 83, 230–244.
4.
Huang, X., et al. (2014). DSKmeans: A new kmeans-type approach to discriminative subspace clustering. Knowledge-Based Systems, 70, 293–300.
5.
Huang, X., Ye, Y., & Zhang, H. (2014). Extensions of kmeans-type algorithms: A new clustering framework by integrating intracluster compactness and intercluster separation. IEEE Transactions on Neural Networks and Learning Systems, 25(8), 1433–1446.
6.
Selvanambi, R., & Natarajan, J. (2016). Cyclic repeated patterns in sequential pattern mining based on the fuzzy CMeans clustering and association rule mining technique. International Journal of Intelligent Engineering & Systems, 1, 176–185.
7.
Martínez-Pérez, Á. (2018). A density-sensitive hierarchical clustering method. Journal of Classification, 35(3), 481–510.MathSciNetMATH
8.
Triayudi, A., & Fitri, I. (2018). Comparision of parameter-free agglomerative hierarchical clustering methods. ICIC Express Letters, 12(10), 973–980.
9.
Xiao, X., Ding, S., & Shi, Z. (2018). An improved Density peaks clustering algorithm with fast finding cluster centers. Knowledge-Based Systems, 158, 65–74.
10.
Cui, G., Li, X., & Dong, Y. (2018). Subspace clustering guided convex nonnegative matrix factorization. Neurocomputing, 292, 38–48.
11.
Deng, Z., Choi, K.-S., Jiang, Y., Wang, J., & Wang, S. (2016). A survey on soft subspace clustering. Information Sciences, 348, 84–106.MathSciNetMATH
12.
Hu, H., Lin, Z., Feng, J., & Zhou, J. (2004) Smooth Representation Clustering. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 3834–3841).
13.
Elhamifar, E., & Vidal, R. (2009). Sparse subspace clustering. In Conference on Computer Vision and Pattern Recognition (pp. 2790–2797).
14.
Liu, G., et al. (2013). Robust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1), 171–184.
15.
Lu, C.-Y. et al. (2012). Robust and efficient subspace segmentation via least squares regression. In European Conference on Computer Vision (pp. 347–360).
16.
Zhen, L., Peng, D., Wang, W., & Yao, X. (2020). Kernel truncated regression representation for robust subspace clustering. Information Sciences, 524, 59–76.MathSciNet
17.
Pourkamali-Anaraki, F., Folberth, J., & Becker, S. (2020). Efficient solvers for sparse subspace clustering. Signal Processing, 172, 1.
18.
Zhong, G., & Pun, C.-M. (2020). Subspace clustering by simultaneously feature selection and similarity learning. Knowledge-Based Systems, 193, 1.
19.
You, C.-Z., & Wu, X.-J. (2018). Feature selection embedded subspace clustering with low-rank and locality constraints. In IEEE International Smart Cities Conference (ISC2) (pp. 1–8).
20.
Abdolali, M., & Rahmati, M. (2020). Neither global nor local: A hierarchical robust subspace clustering for image data. Information Sciences, 514, 333–353.
21.
Zheng, Q., et al. (2020). Feature concatenation multi-view subspace clustering. Neurocomputing, 379, 89–102.
22.
Weng, W., Zhou, W., Chen, J., Peng, H., & Cai, H. (2020). Enhancing multi-view clustering through common subspace integration by considering both global similarities and local structures. Neurocomputing, 378, 375–386.
23.
Kelkar, B. A., Rodd, S. F., & Kulkarni, U. P. (2019). Estimating distance threshold for greedy subspace clustering. Expert Systems with Applications, 135, 219–236.
24.
Guillon, A., Lesot, M.-J., & Marsala, C. (2019). A proximal framework for fuzzy subspace clustering. Fuzzy Sets and Systems, 366, 34–45.MathSciNetMATH
25.
Harikumar, S., & Kaimal, M. R. (2019). SubspaceDB: In-database subspace clustering for analytical query processing. Data & Knowledge Engineering, 121, 109–129.
26.
Yang, Y., & Zhang, X. (2019) Block-diagonal subspace clustering with laplacian rank constraint. In IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) (pp 1556–1559).
27.
Cheng, W., Chow, T. W. S., & Zhao, M. (2016). Locality constrained-ℓp sparse subspace clustering for image clustering. Neurocomputing, 205, 22–31.
28.
Li, Q., Liu, W., & Li, L. (2018). Affinity learning via a diffusion process for subspace clustering. Pattern Recognition, 84, 39–50.
29.
Dong, W., & Xiao-jun, W. (2019). Robust affine subspace clustering via smoothed ℓ0ℓ0 -norm. Neural Processing Letters, 50, 785–797.
30.
Babaeian, A., Babaee, M., Bayestehtashk, A., & Bandarabadi, M. (2015). Nonlinear subspace clustering using curvature constrained distances. Pattern Recognition Letters, 68(1), 118–125.
31.
Chen, J., Zhang, H., Mao, H., Sang, Y., & Yi, Z. (2016). Symmetric low-rank representation for subspace clustering. Neurocomputing, 173(3), 1192–1202.
32.
Guo, Y., Gao, J., & Li, F. (2015). Random spatial subspace clustering. Knowledge-Based Systems, 74, 106–118.
33.
Fackeldey, K., Sikorski, A., & Weber, M. (2018). Spectral clustering for non-reversible Markov chains. Computational and Applied Mathematics, 37(5), 6376–6391.MathSciNetMATH
34.
Liang, R., Bai, Y., & Lin, H. X. (2019). An inexact splitting method for the subspace segmentation from incomplete and noisy observations. Journal of Global Optimization, 73, 411–429.MathSciNetMATH
35.
Zhu, W., Jiwen, L., & Zhou, J. (2018). Nonlinear subspace clustering for image clustering. Pattern Recognition Letters, 107, 131–136.
36.
Bai, L., Liang, J., & Guo, Y. (2018). An ensemble clusterer of multiple fuzzy k-means clusterings to recognize arbitrarily shaped clusters. IEEE Transactions on Fuzzy Systems, 26(6), 3524–3533.
37.
Chen, J., Zheng, H., Lin, X., Yangyang, W., & Mengmeng, S. (2018). A novel image segmentation method based on fast density clustering algorithm. Engineering Applications of Artificial Intelligence, 73, 92–110.
38.
Choy, S. K., Lam, S. Y., Yu, K. W., Lee, W. Y., & Leung, K. T. (2017). Fuzzy model-based clustering and its application in image segmentation. Pattern Recognition, 68, 141–157.
39.
Zhao, F., Liu, H., & Fan, J. (2015). A multiobjective spatial fuzzy clustering algorithm for image segmentation. Applied Soft Computing, 30, 48–57.
40.
Zhang, X., & Sun, Y. (2017). Improved fuzzy clustering for image segmentation based on local and non-local information. In International Conference on Security, Pattern Analysis, and Cybernetics (pp. 49–54).
41.
Guo, L., Chen, L., Wu, Y., & PhilipChen, C. L. (2016). Image guided fuzzy clustering for image segmentation. International Conference on Systems, Man, and Cybernetics, 1, 004271–004276.
42.
Liu, G., Zhang, Y., & Wang, A. (2015). Incorporating adaptive local information into fuzzy clustering for image segmentation. IEEE Transactions on Image Processing, 24(11), 3990–4000.MathSciNetMATH
43.
Saikumar, T., Yojana, K., Madhava Rao, C., & Murthy, P. S. (2012). Fast Improved Kernel Fuzzy C-Means (IKFCM) clustering for image segmentation on level set method. In International conference on advances in engineering; Science and management (pp. 445–449).
44.
Zhao, F., & Jiao, L. (2011). Spatial improved fuzzy c-means clustering for image segmentation. In International Conference on Electronic & Mechanical Engineering and Information Technology (pp. 4791–4794).
45.
Rajeswari, M., Wei, B. C., & Yeow, L. S. (2010) Spatial Multiple Criteria Fuzzy Clustering for Image Segmentation. In Second International Conference on Computational Intelligence; Modelling and Simulation (pp. 305–310).
46.
Sulaiman, S. N., & Isa, N. A. M. (2010). Adaptive fuzzy-K-means clustering algorithm for image segmentation. IEEE Transactions on Consumer Electronics, 4, 2661–2668.
47.
Zhu, F., Song, Y., & Chen, J. (2010). Fuzzy C-means clustering for image segmentation using the adaptive spatially median neighborhood information. In Chinese Conference on Pattern Recognition (CCPR) (pp. 1–5).
48.
Morales, E. R. C., & Mendizabal, Y. Y. (2010) Contiguity-constrained hierarchical clustering for image segmentation. In 2nd International Conference on Image Processing Theory; Tools and Applications (pp. 279–283).
49.
Wicaksono, Y. A., Rizaldy, A., Fahriah, S., & Soeleman, M. A. (2017) Improve image segmentation based on closed form matting using K-means clustering. In International Seminar on Application for Technology of Information and Communication (iSemantic) (pp. 26–30).
50.
Li, Z., & Tang, Y. (2018). Comparative density peaks clustering. Expert Systems with Applications, 95, 236–247.
51.
Myhre, J. N., Mikalsen, K. Ø., Løkse, S., & Jenssen, R. (2018). Robust clustering using a kNN mode seeking ensemble. Pattern Recognition, 76(491–505), 2018.
52.
Hou, J., Liu, W., Xu, E., & Cui, H. (2016). Towards parameter-independent data clustering and image segmentation. Pattern Recognition, 60, 25–36.
53.
Singh, P., & Meshram, P. A. (2017) Survey of density based clustering algorithms and its variants. In International Conference on Inventive Computing and Informatics (pp. 920–926).
54.
Du, H., Fang, W., Huang, H., & Zeng, S. (2018). MMDBC: Density-based clustering algorithm for mixed attributes and multi-dimension data. In International Conference on Big Data and Smart Computing (pp. 549–552).
55.
Zhu, Y., & Huang, C. (2012). An adaptive histogram equalization algorithm on the image gray level mapping. Physics Procedia, 25, 601–608.
56.
Juneja, K. (2017). A noise robust VDD composed PCA-LDA model for face recognition. In International Conference on Information, Communication and Computing Technology (pp. 216–229).
57.
Martinez, A. M., & Benavente, R. (1998) The AR Face Database.
Metadaten
Titel
Multi-Featured and Fuzzy Based Dual Analysis Approach to Optimize the Subspace Clustering for Images
verfasst von
Kapil Juneja
Publikationsdatum
19.05.2020
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2020
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07482-0

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