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We describe an approach to identifying specific settings in large collections of photographs corresponding to a visual diary. An algorithm developed for setting detection should be capable of clustering images captured at the same real world locations (e.g. in the dining room at home, in front of the computer in the office, in the park, etc.). This requires the selection and implementation of suitable methods to identify visually similar backgrounds in images using their visual features. The goal of the work reported here is to automatically detect settings in images taken over a single week. We achieve this using scale invariant feature transform (SIFT) features and X-means clustering. In addition, we also explore how the use of location based metadata can aid this process.