Multispectral human co-segmentation via joint convolutional neural networks

We present a novel human body co-segmentation method for unregistered multispectral, color and thermal, images by leveraging CNNs. The main challenges for that tasks are no-alignment between color and thermal images and an absent of ground truth human segmentation labels. To solve these limitations, our key-insight is to formulate the segmentation network for each modality that solve two sub-tasks, correspondence and classification, in a joint and iterative manner. We formulate the learning framework between multispectral images in a way that training labels for one modality are used to learn the network for the other modality. We estimate dense correspondences between multispectral image pairs using intermediate convolutional activations of CNNs and perform human segmentation for each modality through the conditional random fields (CRF) optimization using unary and pairwise fusion. These two steps are formulated as an iterative framework, enables the network to converge on an optimal solution. Experimental results show that our proposed method outperforms conventional state-of-the-art methods on the VAP benchmark consisting of unregistered multispectral color and thermal images.