Interactive Image Segmentation Algorithm Combining Multiple Histogram and SVM
Interactive image segmentation has important applications in many fields such as image editing,medical image analysis.However,many interactive image segmentation algorithms are highly dependent on user interaction information,and can-not use a small amount of information to accurately extract the target object.To address the above problems,interactive image seg-mentation combining histogram and support vector machine(MHSVM)is proposed.Given a small number of user input markers,the SLIC method is adopted to segment the original image into several irregular regions,and both the color histogram and gradient orientation histogram are applied as the feature vector of each region,then region merging is done according to the merging rule.Then,and the training samples are constructed,the number of positive and negative samples is balanced.Finally,SVM classifier is co-trained to classify the remaining unlabeled superpixels.The experimental results show that MHSVM extracts foreground objects successfully from the background.MHSVM is less affected by the user input markers in compared with the state-of-the-art interac-tive image segmentation methods,which has obvious advantages in segmentation accuracy.
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