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We propose a novel method for predicting image labels by fusing image contentdescriptors with the social media context of each image. An image uploaded to asocial media site such as Flickr often has meaningful, associated information,such as comments and other images the user has uploaded, that is complementaryto pixel content and helpful in predicting labels. Prediction challenges suchas ImageNet~\cite{imagenet_cvpr09} and MSCOCO~\cite{LinMBHPRDZ:ECCV14} use onlypixels, while other methods make predictions purely from social media context\cite{McAuleyECCV12}. Our method is based on a novel fully connectedConditional Random Field (CRF) framework, where each node is an image, andconsists of two deep Convolutional Neural Networks (CNN) and one RecurrentNeural Network (RNN) that model both textual and visual node/image information.The edge weights of the CRF graph represent textual similarity and link-basedmetadata such as user sets and image groups. We model the CRF as an RNN forboth learning and inference, and incorporate the weighted ranking loss andcross entropy loss into the CRF parameter optimization to handle the trainingdata imbalance issue. Our proposed approach is evaluated on the MIR-9K datasetand experimentally outperforms current state-of-the-art approaches.

Deep Neural Networks In Fully Connected CRF For Image Labeling With Social Network Metadata