Image smoothing via L0 gradient minimization
Proceedings of the 2011 SIGGRAPH Asia Conference
Learning-Based symmetry detection in natural images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Spectral Image Segmentation Using Image Decomposition and Inner Product-Based Metric
Journal of Mathematical Imaging and Vision
A genetic algorithm for color image segmentation
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Graph-based joint clustering of fixations and visual entities
ACM Transactions on Applied Perception (TAP)
Skin lesion image segmentation using a color genetic algorithm
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
The Journal of Machine Learning Research
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We present a modification of "Normalized Cuts" to incorporate priors which can be used for constrained image segmentation. Compared to previous generalizations of "Normalized Cuts" which incorporate constraints, our technique has two advantages. First, we seek solutions which are sufficiently "correlated" with priors which allows us to use noisy top-down information, for example from an object detector. Second, given the spectral solution of the unconstrained problem, the solution of the constrained one can be computed in small additional time, which allows us to run the algorithm in an interactive mode. We compare our algorithm to other graph cut based algorithms and highlight the advantages.