Learn Image Object Co-segmentation with Multi-scale Feature Fusion
- Publication Type:
- Conference Proceeding
- Citation:
- 2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019, 2019
- Issue Date:
- 2019-12-01
Open Access
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
© 2019 IEEE. Image object co-segmentation aims to segment common objects in a group of images. This paper proposes a novel neural network, which extracts multi-scale convolutional features at multiple layers via a modified VGG network and fuses them both within and across images as the intra-image and the inter-image features. Then these two kinds of features are further fused at each scale as the multi-scale co-features of common objects, and finally the multi-scale co-features are summed up and upsampled to obtain the co-segmentation results. To simplify the network and reduce the rapidly rising resource cost along with the inputs, the reduced input size, less downsampling and dilation convolution are adopted in the proposed model. Experimental results on the public dataset demonstrate that the proposed model achieves a comparable performance to the state-of-The-Art co-segmentation methods while the computation cost has been effectively reduced.
Please use this identifier to cite or link to this item: