Crowdsourcing is a new computing paradigm that harnesses human effort to solve computer-hard problems. Budget and quality are two fundamental factors in crowdsourcing, but they are antagonistic and their balance is crucially important. Induction and inference are principled ways for humans to acquire knowledge. Transfer learning can also enable induction and inference processes. When a new task comes, we may not know how to go about approaching it. On the other hand, we may have easy access to relevant knowledge that can help us with the new task. As such, via appropriate knowledge transfer, for example, an improved annotation can be achieved for the task at a small cost. To make this idea concrete, we introduce the Crowdsourcing with Multiple-source Knowledge Transfer (CrowdMKT)approach to transfer knowledge from multiple, similar, but different domains for a new task, and to reduce the negative impact of irrelevant sources. CrwodMKT first learns a set of concentrated high-level feature vectors of tasks using knowledge transfer from multiple sources, and then introduces a probabilistic graphical model to jointly model the tasks with high-level features, workers, and their annotations. Finally, it adopts an EM algorithm to estimatethe workers strengths and consensus. Experimental results on real-world image and text datasets prove the effectiveness of CrowdMKT in improving quality and reducing the budget.