An unsupervised boundary detection algorithm based on orientation contrast model

For large image sets on the Web, due to the absense of a ground truth boundary or the high cost of getting one, an unsupervised boundary detection algorithm based on orientation contrast model was proposed. The model is especially suited for detecting object boundaries surrounded by natural textures. In the Rug image database, the algorithm outperforms the state-of-the-art unsupervised boundary detection algorithm, which verifies the validity of the model.