CoSegNet: Image Co-segmentation Using a Conditional Siamese Convolutional
Network
Abstract
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 stateof-the-art methods on challenging co-segmentation
datasets