Hybrid Saliency-SVM Method Implementation for Automatic Data Training Selection in Image Segmentation

Abstract

Image segmentation is one of the most important step in computer vision and image processing, which later will be used in image retrieval, object identifying and data classification. Image segmentation can be seen as classification problem, namely by marking each pixels according to certain characteristics. Support Vector Machine (SVM) is a classification method included in supervised learning. Supervised learning is a method which requires training and testing. Training sample used in training process isn’t always exist in few cases, especially in the image segmentation case. This particular research implemented SVM-based method which is Saliency-SVM for automatic data training selection in image segmentation. This method generates data training using SVM-based visual saliency detection where there is pre-segmentation step and trimap formation based on saliency information from visual saliency detection, HSV color space quantitation, histogram analysis and local homogeneity threshold. Data training produced is pixel belonging to positive (object) and negative (background). The step before segmentation done with SVM is feature extraction to create input vector in SVM. Object segmentation on the image is done by SVM based on SVM Trained Model. Test result from Saliency-SVM for this image segmentation has average accuracy value up to 94,84 % compared to the image ground truth