Adaptable image segmentation via simple pixel classification

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.

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

“…A wide range of satellite image segmentations have also been reported, with case studies including the detection of shorelines [39], burned areas [40,41], or forest variables [42] and change detection in wetlands [43]. If a trackable parametrisation exists, similar to image classification, then it can be used directly with no loss of information in segmentation [44]. In such cases, the strategy of object detection in segmentation algorithms is based on the identification of the regions on the image which present an assembly of contiguous pixels that meet threshold criteria [45,46].…”

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

“…This operation is an important part of image processing that attempts to clarify the brain tissue details along with decreasing the impact of noise. The proper preprocessing operation helps the system to get better results in the next steps, especially image segmentation …”

“…The proper F I G U R E 1 Some samples of brain tumor dermoscopic images 1 preprocessing operation helps the system to get better results in the next steps, especially image segmentation. [14][15][16] The next step is image segmentation. The process of splitting the image into its components to extract the desired objects is called image segmentation.…”