Seed growing for interactive image segmentation using SVM classification with geodesic distance

• Published in: Electronics letters 2017-01, Vol.53 (1), p.22-24
• Main Authors: Park, S, Lee, H.S, Kim, J
• Format: Article
• Language: English
• Subjects: Classification; Classifiers; differential geometry; Electronics; geodesic distance; Geodesy; Image and vision processing and display technology; image classification; Image segmentation; interactive image segmentation; learning (artificial intelligence); nonseed superpixel classification; public benchmark dataset; Seeds; seed‐growing method; supervised classification framework; support vector machine; Support vector machines; SVM classification; training
• ISSN: 0013-5194; 1350-911X; 1350-911X
• DOI: 10.1049/el.2016.3919

In an interactive image segmentation, the quantity of a user-given seed is known to affect the segmentation accuracy. In this Letter, we propose a seed-growing method expanding the quantity of a seed to reduce the bias of the given seed and improve the segmentation accuracy. To grow the given seed, a supervised classification framework with geodesic distance features is proposed. From a single input image, a support vector machine (SVM) classifier is trained on the seed superpixels of an input image. Other non-seed superpixels are then classified into object, background and non-seed regions by the trained classifier. In experiments, the proposed method showed promising results by improving the segmentation accuracy of existing segmentation methods in public benchmark datasets.