A multiscale algorithm for image segmentation by variational method
SIAM Journal on Numerical Analysis
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color image segmentation guided by a color gradient network
Pattern Recognition Letters
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Automatic seeded region growing for color image segmentation
Image and Vision Computing
Interactive Image Segmentation via Adaptive Weighted Distances
IEEE Transactions on Image Processing
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow
IEEE Transactions on Image Processing
Learning a nonlinear distance metric for supervised region-merging image segmentation
Computer Vision and Image Understanding
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In this paper we describe an experiment where we studied empirically the application of a learned distance metric to be used as discrimination function for an established color image segmentation algorithm. For this purpose we chose the Mumford-Shah energy functional and the Mahalanobis distance metric. The objective was to test our approach in an objective and quantifiable way on this specific algorithm employing this particular distance model, without making generalization claims. The empirical validation of the results was performed in two experiments: one applying the resulting segmentation method on a subset of the Berkeley Image Database, an exemplar image set possessing ground-truths and validating the results against the ground-truths using two well-known inter-cluster validation methods, namely, the Rand and BGM indexes, and another experiment using images of the same context divided into training and testing set, where the distance metric is learned from the training set and then applied to segment all the images. The obtained results suggest that the use of the specified learned distance metric provides better and more robust segmentations, even if no other modification of the segmentation algorithm is performed.