Mobile augmented reality applications rely on automatically recognising a visual scene through matching of derived image features. To ensure the Quality of Experience (QoE) perceived by users, such applications should achieve high matching accuracy meanwhile minimizing the waiting time to meet real-time requirement. An efficient solution is to develop an effective feature selection method to select the most robust features against distortions caused by camera capture to achieve high matching accuracy whilst transmission and matching process of the features are significant reduced. Feature selection is also beneficial to reducing the computational complexities of the matching system so that waiting time is minimized and hence user QoE is maximised. In this paper, a QoE estimation for state-of-the-art feature selection in MPEG-7 CDVS based on waiting time and matching accuracy as judged by retrieval experiments on a realistic image dataset with real-world distortions caused by image capture is analysed. The predicted QoE results suggest that feature selection can provide good QoE to users.