Multi-Scale Contourlet Knowledge Guide Learning Segmentation

For accurate segmentation, effective feature extraction has always been a challenging problem, since the variability of appearance and the fuzziness of object boundaries. Convolutional neural networks have recently gained recognition in feature representation learning. However, it is only conducted in the spatial domain, and lacks effective representation of directionality, singularity and regularity in the spectral domain for anomaly detection of images. This is the key to feature learning representation of high-order singularity. To solve this problem, a multi-scale contourlet knowledge guide learning network is proposed in this paper. It is novel in this sense that, different from the CNNs in the spatial domain, the proposed method learns the multi-scale contourlet sparse representation to obtain more effective and sparse features in multi-scales and multi-directions. Furthermore, the contourlet knowledge guide learning can enhance the representation of spectral domain features. It is shown that the proposed network can learn the multi-level discriminative features and capture the more accurate object boundaries. The segmentation ability in theoretical analysis and experiments on five polyp segmentation datasets (CVC-ColonDB, CVC-ClinicDB, Kvasir-SEG, ETIS-LaribPolypDB, EndoSceneStill) and two building datasets (Massachusetts, WHU) are compared with developed methods. It must be emphasized that there is potential in effective feature learning representation and the generalization capability of the proposed method in deep learning, recognition and interpretation.