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.

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 state of-the-art

methods on challenging co-segmentation

datasets.