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Seeing by touching: Cross-modal matching for tactile and vision measurements

In this paper, we investigate the visual-tactile cross-modal matching problem which is formulated as retrieving the relevant samples in unlabeled gallery visual dataset in response to the tactile query sample. Such a problem exhibits non-trivial challenges because there does not exist sample-to-sample pairing relation between tactile and visual modalities, which exhibit significantly different characteristics. To this end, we design a dictionary learning model which can simultaneously learn the projection subspace and the latent common dictionary for the visual and tactile measurements. In addition, an optimization algorithm is developed to effectively solve the common dictionary learning problem. Based on the obtained solutions, the visual-tactile cross-modal matching method can be easily established. Finally, we perform experimental validations on the PHAC-2 datasets to show the effectiveness of the proposed visual-tactile cross-modal matching framework and method.