Combine multiple classifiers to build a multi-modal classifier
Basically, you can do one of two things: 


Combine features from both classifiers. I.e., instead of SVM-text and SVM-image you may train single SVM that uses both - textual and visual features. 
Use ensemble learning. If you already have probabilities from separate classifiers, you can simply use them as weights and compute weighted average. For more sophisticated cases there are Bayesian combiners (each classifier has its prior), boosting algorithms (e.g. see AdaBoost) and others. 


Note, that ensembles where initially created for combining different learners, not different sets if features. In this later case ensembles have advantage mostly in cases when different kinds of features just can't be combined in a single vector efficiently. But in general, combing features is simpler and more straightforward.