An l _½ and Graph Regularized Subspace Clustering Method for Robust Image Segmentation

Abstract

Segmenting meaningful visual structures from an image is a fundamental and most-addressed problem in image analysis algorithms. However, among factors such as diverse visual patterns, noise, complex backgrounds, and similar textures present in foreground and background, image segmentation still stands as a challenging research problem. In this article, the proposed method employs an unsupervised method that addresses image segmentation as subspace clustering of image feature vectors. Initially, an image is partitioned into a set of homogeneous regions called superpixels, from which Local Spectral Histogram features are computed. Subsequently, a feature data matrix is created whereupon subspace clustering methodology is applied. A single-stage optimization model is formulated with enhanced segmentation capabilities by the combined action of l ½ and l 2 norm minimization. Robustness of l ½ regularization toward both the noise and overestimation of sparsity provides simultaneous noise robustness and better subspace selection, respectively. While l 2 norm facilitates grouping effect. Hence, the designed optimization model ensures an improved sparse solution and a sparse representation matrix with an accurate block diagonal structure, which thereby favours getting properly segmented images. Then, experimental results of the proposed method are compared with the state-of-art algorithms. Results demonstrate the improved performance of our method over the state-of-art algorithms.

Image Segmentation and Grouping Using Graph Method (SLIC Algorithm)

“…Lakshmi and Kumar (2022) point out that no single method is suitable for all image types, and not all methods can be applied to every image. Francis et al (2022) further note the difficulty, due to factors like complex visual patterns, poor image quality, and similar-looking foreground and background elements. Jiao (2022) also discusses the problem of unsupervised segmentation, which involves creating segments within an image without prior knowledge of its contents.…”

“…However, image segmentation faces many challenges such as lack of universally applicable approaches, presence of noise, complex backgrounds and similar textures in foreground and background (Francis, Baburaj & George, 2022;Lakshmi & Anil Kumar, 2022). Unsupervised image segmentation which generates semantic segments without prior knowledge is also challenging (Jiao, 2022).…”

A Survey on High-Dimensional Subspace Clustering

“…In addition, the optimal solution obtained by CAWR is proved to have the block diagonal structure under noiseless data and independent subspace. However, CAWR lacks robustness to data corruption [91]. Instead of using the convex approximations of rank and l 0 pseudo norm, Brbić and Kopriva [80] introduced two S 0 /l 0 pseudo norm-based nonconvex regularizations for LRSSC.…”

“…To deal with the challenges brought by diverse visual patterns, noise, and complex background in image processing, Francis et al [91] proposed the following subspace clustering method by integrating the l 1/2 -norm, l 2 -norm and graph Laplacian regularization (GLl 1/2 RSC). The proposed GLl 1/2 RSC can be formulated as min…”