Affiliation:
1. National Institute of Technology Calicut, Calicut, Kerala, India
2. Government Engineering College Kozhikode, Calicut, Kerala, India
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.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture
Reference52 articles.
1. A survey on evaluation methods for image segmentation
2. Rafael C. Gonzales and Richard E. Woods. 2002. Digital Image Processing (3rd Edition). Prentice-Hall Inc..
3. Understanding Deep Learning Techniques for Image Segmentation
4. Image segmentation by correlation adaptive weighted regression
5. Zhengqin Li and Jiansheng Chen. 2015. Superpixel segmentation using linear spectral clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1356–1363.
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