LAUSR

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.