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Domain adaptation is a method to classify the new domain accurately by using the marked image of the old domain. It shows a good but a challenging application prospect in computer vision. In this article, we propose a unified and optimized problem modeling method, which is called as Geodesic Kernel embedding Distribution Alignment (GKDA). Specifically, GKDA aims to reduce the domain differences. GKDA avoids degenerated feature transformation by using geodesic kernel mapping feature, and then adjusts the weight of cross-domain instances in the process of dimensionality reduction in principle, finally, constructs a new feature to represent the difference of distribution and unrelated instances. The experiment result shows that GKDA has obvious superiority in cross-domain image recognition.

The traditional uniform distribution algorithm does not filter the image data when extracting the approximate features of text-image data under the event, so the similarity between the image data and the text is low, which leads to low accuracy of the algorithm. This paper proposes a text-image feature mapping algorithm based on transfer learning. The existing data is filtered by ‘clustering technology’ to obtain similar data with the target data. The significant text features are calculated through the latent Dirichlet allocation (LDA) model and information gain based on Gibbs sampling. Bag of visual word (BOVW) model and Naive Bayesian method are used to model image data. With the help of the text-image co-occurrence data in the same event, the text feature distribution is mapped to the image feature space, and the feature distribution of image data under the same event is approximated. Experimental results show that the proposed algorithm can obtain the feature distribution of image data under different events, and the average cosine similarity is as high as 92%, the average dispersion is as low as 0.06%, and the accuracy of the algorithm is high.

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