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.
This paper presents algorithms and their software implementation, which assess the quality of segmentation of any image, given an ideal segmentation (or ground truth image) and a usually less-than-ideal segmentation result (or machine segmented image). The software first identifies every region in both the ground truth and machine segmented images, establishes as much correspondence as possible between the images, then computes two sets of measures of quality: one, region-based and the other, pixel-based. The paper describes the algorithms used to assess quality of segmentation and presents results of the application of the software to images from the Berkeley Segmentation Dataset. The software, which is freely available for download, facilitates R&D work in image segmentation, as it provides a tool for assessing the results of any image segmentation algorithm, allowing developers of such algorithms to focus their energies on solving the segmentation problem, and enabling them to tests large sets of images, swiftly and reliably.
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