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Published in:
2023 | OriginalPaper | Chapter
7. Co-segmentation Using Graph Convolutional Network
Authors : Avik Hati, Rajbabu Velmurugan, Sayan Banerjee, Subhasis Chaudhuri
Published in: Image Co-segmentation
Publisher: Springer Nature Singapore
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Abstract
This chapter describes a supervised method for solving the co-segmentation problem for an image pair by training a graph convolutional neural network (GCNN)-based classifier. First each image pair is represented as a weighted graph through superpixel segmentation, and intra-image and inter-image superpixel feature similarities. The network takes the graph as input, and learns to classify each node of the graph into either the common foreground or the background. During training, an additional classifier is used that learns to classify the entire graph into the semantic class the common object belongs to. The entire network is trained end-to-end using a combination of the binary cross-entropy loss and the categorical cross-entropy loss.