To ensure that all important moments of an event are represented and that challenging scenes are correctly captured, both amateur and professional photographers often opt for taking large quantities of photographs. As such, they are faced with the tedious task of organizing large collections and selecting the best images among similar variants. Automatic methods assisting with this task are based on independent assessment approaches, evaluating each image apart from other images in the collection. However, the overall quality of photo collections can largely vary due to user skills and other factors. In this work, we explore the possibility of context-aware image quality assessment, where the photo context is defined using a clustering approach, and statistics of both the extracted context and the entire photo collection are used to guide identification of low-quality photos. We demonstrate that our method is able to flexibly adapt to the nature of processed albums and to facilitate the task of image selection in diverse scenarios.