Abstract
The semantic correlation matching algorithm, which attempts to integrate semantic information based on the unified space obtained by CCA learning, achieves an improvement in the effect of cross-modal retrieval. However, there is still a lot of room for the algorithm to make further progress in the generation of shared subspace. This paper proposes a semantic correlation matching algorithm based on the single-layer feed forward network. It generates features through neural networks to represent weight and bias. Meanwhile, it maximizes the correlation between different modalities by means of cross-coupling. The experimental results show that the algorithm proposed in this paper is superior to the compared method in terms of cross-modal retrieval task of image-text and has better cross-modal retrieval ability.
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