Most existing fusion methods are suitable in situations where the image classification method is identical with the text classification method. However

better classification methods of image and text for image-text co-occurrence data are not identical in many application scenarios. The decision benchmarks of different classification methods are not unified

which would reduce the classification precision of the fusion method. The fusion method based on KNN with weight adjustment for the classification of image-text co-occurrence data is proposed to overcome the problem. First

the softmax and multiple classification SVM are used to classify the image and text. The image's KNN model and the text's KNN model are constructed using the weighted classification decision values of image and text instances

which are correctly discriminated on a training dataset. Then

the classification decision values of the test instance are predicted by the image's KNN model and the text's KNN model. The image classification probability and the text classification probability of the test instance are determined by the number of each category in the nearest neighbors

and the classification probabilities would unify the classification decision benchmarks. Finally

the fusion coefficient is calculated by the number of image instances and the number of text instances discriminated correctly on a training dataset applied to fuse the classification probabilities of the image and text for the test instance. We performed an experiment on the Attribute Discovery dataset and compared the proposed fusion method with the baseline method. Experimental results show that the proposed fusion method achieved higher classification precision than the image classification method and text classification method

and the proposed fusion method increased the average classification precision by 4.45%. Moreover

the proposed fusion method increased the average information integration ability for image-text co-occurrence data by 4.19%. The proposed fusion method unifies the classification decision benchmarks of the different classification methods for image and text and implements the effective fusion of image-text co-occurrence data. Therefore

the proposed fusion method has a better ability to integrate information

with a better classification performance.