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The gap between low-level visual signals and high-level semantics has beenprogressively bridged by continuous development of deep neural network (DNN).With recent progress of DNN, almost all image classification tasks haveachieved new records of accuracy. To extend the ability of DNN to imageretrieval tasks, we proposed a unified DNN model for image-query similaritycalculation by simultaneously modeling image and query in one network. Theunified DNN is named the cross space mapping (CSM) model, which contains twoparts, a convolutional part and a query-embedding part. The image and query aremapped to a common vector space via these two parts respectively, andimage-query similarity is naturally defined as an inner product of theirmappings in the space. To ensure good generalization ability of the DNN, welearn weights of the DNN from a large number of click-through logs whichconsists of 23 million clicked image-query pairs between 1 million images and11.7 million queries. Both the qualitative results and quantitative results onan image retrieval evaluation task with 1000 queries demonstrate thesuperiority of the proposed method.

Learning cross space mapping via DNN using large scale click-through logs