Semantic scene classification is an open problem in image understanding, especially when information purely from image content (i.e., pixels) is employed. However, in applications involving image collections, surrounding images give each image a temporal context. We present a probabilistic approach to scene classification, capable of integrating both image content and temporal context. Elapsed time between images can be derived from the timestamps recorded by digital cameras. Our temporal context model is trained to exploit the stronger dependence between images captured within a short period of time, indicated by the elapsed time. We demonstrate the efficacy of our approach by applying it to the problem of indoor-outdoor scene classification and achieving significant gains in accuracy. The probabilistic temporal context model can be applied to other scene classification problems.