Adaptable image segmentation via simple pixel classification. (2nd May 2018)

• Record Type: Journal Article
• Authors: Kharma, Nawwaf; Mazhurin, Anton; Saigol, Kamil; Sabahi, Farzad
• Is Part Of: Computational intelligence. Volume 34:Number 2(2018)
• Page Start: 734
• Page End: 762
• Publication Date: 2018-05-02
• Subjects: image segmentation -- machine learning -- parallel processing -- pixel classification; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12173
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: We propose an approach to image segmentation that views it as one of pixel classification using simple features defined over the local neighborhood. We use a support vector machine for pixel classification, making the approach automatically adaptable to a large number of image segmentation applications. Since our approach utilizes only local information for classification, both training and application of the image segmentor can be done on a distributed computing platform. This makes our approach scalable to larger images than the ones tested. This article describes the methodology in detail and tests it efficacy against 5 other comparable segmentation methods on 2 well‐known image segmentation databases. Hence, we present the results together with the analysis that support the following conclusions: (i) the approach is as effective, and often better than its studied competitors; (ii) the approach suffers from very little overfitting and hence generalizes well to unseen images; (iii) the trained image segmentation program can be run on a distributed computing environment, resulting in linear scalability characteristics. The overall message of this paper is that using a strong classifier with simple pixel‐centered features gives as good or better segmentation results than some sophisticated competitors and does so in a computationally scalable fashion.