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In this paper we investigate the problem of matching images and captions. We exploit the kernel canonical correlation analysis (KCCA) to learn a similarity between images and texts. We then propose methods to build improved visual and text kernels. The visual kernels are based on visual classifiers that use responses of a deep convolutional neural network as features, and the text kernel improves the Bag-of-Words (BoW) representation by learning a vision based lexical similarity between words. We consider two application scenarios, one where only an external image set weakly related to the evaluation dataset is available for training the visual classifiers, and one where visual data closely related to the evaluation set can be used. We evaluate our visual and text kernels on a large and publicly available benchmark, where we show that our proposed methods substantially improve upon the state-of-the-art.

Leveraging High Level Visual Information for Matching Images and Captions