A Joint Sparse and Correlation Induced Subspace Clustering Method for Segmentation of Natural Images

Due to the presence of diverse and disparate patterns, the segmentation of natural images endures crucial as well as a challenging problem in image analysis algorithms. The proposed method addresses image segmentation as a subspace clustering of image feature vectors. Initially, an image is partitioned into superpixels and further, a feature data matrix is computed using the Local Spectral Histogram (LSH) features from individual superpixels. A single-stage optimization model is formulated which incorporates better subspace selection, excellent grouping effect and simultaneous noise robustness for the uncorrelated, correlated and corrupted data by the conjunctive venture of l 1 , l 2 and l 2,1 norm minimization. The proposed model is solved using Augmented Lagrangian technique. We compared the proposed method with state-of-the-art methods and the results demonstrate the improved performance of our proposed model over the existing counterparts.