Adaptable image segmentation via simple pixel classification

作者：Kharma Nawwaf^*; Mazhurin Anton; Saigol Kamil; Sabahi Farzad

来源：Computational Intelligence, 2018, 34(2): 734-762.

DOI：10.1111/coin.12173

• image segmentation
• machine learning
• parallel processing
• pixel classification

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.

• 出版日期2018-5