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Image Segmentation Using Ncut in the Wavelet Domain

A new image segmentation method is developed that combines the advantage of the normalized cuts (Ncut) algorithm to solve the perceptual grouping problem by means of graph partitioning, and the ability of wavelet transform to capture image features by decomposing signal both in time and frequency. We derive image features from orientation histograms defined on the detail subbands of the discrete wavelet transform. The segmentation is implemented by partitioning a graph representing an image at the coarsest transform level, while the weights of the graph are calculated from all the scales. Due to the reduced dimensionality of the dataset, the speed of Ncut is increased. Even though segmentation is carried out at a coarsest level of transform, the results are accurate for images of different structural contents, including textures.