Image segmentation apparatus, method and relevant computing device

Abstract

The present disclosure provides an image segmentation apparatus, method and relevant computing device. The image segmentation apparatus comprises: a feature extractor configured to extract N image semantic features having different scales from an input image, where N is an integer not less than 3; and a feature processor comprising cascaded dense-refine networks and being configured to perform feature processing on the N image semantic features to obtain a binarized mask image for the input image. A dense-refine network is configured to generate a low-frequency semantic feature from semantic features input thereto by performing densely-connected convolution processing on the semantic features respectively to obtain respective image global features, performing feature fusion on the image global features to obtain a fused image global feature, and performing pooling processing on the fused image global feature to generate and output the low-frequency semantic feature. The semantic features are selected from a group consisting of the N image sematic features and low-frequency semantic features generated by dense-refine networks. The feature processor is configured to obtain the binarized mask image based on low-frequency semantic features generated by the dense-refine networks.

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