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The objective in image co-segmentation is to jointlysegment unknown common objects from a givenset of images. In this paper, we propose a noveldeep convolution neural network based end-to-endco-segmentation model. It is composed of a metriclearning and decision network leading to a novelconditional siamese encoder-decoder network forestimating a co-segmentation mask. The role of themetric learning network is to find an optimum latentfeature space where objects of the same classare closer and that of different classes are separatedby a certain margin. Depending on theextracted features, the decision network decideswhether input images have common objects or notand the encoder-decoder network produces a cosegmentationmask accordingly. Key aspects of thearchitecture are as follows. First, it is completelyclass agnostic and does not require any semanticinformation. Second, in addition to producingmasks, the decoder network also learns similarityacross image pairs that improves co-segmentationsignificantly. Experimental results reflect an excellentperformance of our method compared to state of-the-artmethods on challenging co-segmentationdatasets.

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