With the recent dramatic increase in the popularity of mobile electronic devices equipped with cameras, there is a growing number of real-world applications for image classification. Nevertheless, some of these real-world applications aim to classify images captured in an unconstrained manner and in complex environments where existing image classification techniques may not perform well. We propose an efficient image classification system that is robust enough to cope with challenging imaging conditions, and demonstrate its effectiveness in the context of classification of real-world images of dumpsters captured by mobile phones in the Indian metropolitan city of Hyderabad. Our system is able to achieve accurate classification of the cleanliness state of the dumpsters despite the challenging uncontrolled urban environment by utilizing a multi-stage approach, where the first stage is the efficient detection of the dumpster, and the second stage is the classification of its state. We analyze the performance of the system and provide comprehensive experimental results on a real-world public dataset. © 2013 IEEE.