CoSegNet: Image Co-segmentation using a Conditional Siamese Convolutional Network

CoSegNet: Image Co-segmentation using a Conditional Siamese Convolutional Network

Sayan Banerjee, Avik Hati, Subhasis Chaudhuri, Rajbabu Velmurugan

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 673-679. https://doi.org/10.24963/ijcai.2019/95

The objective in image co-segmentation is to jointly segment unknown common objects from a given set of images. In this paper, we propose a novel deep convolution neural network based end-to-end co-segmentation model. It is composed of a metric learning and decision network leading to a novel conditional siamese encoder-decoder network for estimating a co-segmentation mask. The role of the metric learning network is to find an optimum latent feature space where objects of the same class are closer and that of different classes are separated by a certain margin. Depending on the extracted features, the decision network decides whether input images have common objects or not and the encoder-decoder network produces a cosegmentation mask accordingly. Key aspects of the architecture are as follows. First, it is completely class agnostic and does not require any semantic information. Second, in addition to producing masks, the decoder network also learns similarity across image pairs that improves co-segmentation significantly. Experimental results reflect an excellent performance of our method compared to state of-the-art methods on challenging co-segmentation datasets.
Keywords:
Computer Vision: Computer Vision
Machine Learning Applications: Applications of Supervised Learning
Computer Vision: 2D and 3D Computer Vision
Machine Learning: Deep Learning