Our study mainly focus on detecting changed regions in two images of the same scene taken by digital cameras at different times. The images taken by digital cameras generally provide less information than multi-channel remote sensing images. Moreover, the application-dependent insignificant changes, such as shadows or clouds, may cause the failure of the classical methods based on image differences. The machine learning approach seems to be promising, but the lack of a sufficient volume of training data for photographic landscape observatories discards a lot of methods. So we investigate in this work the interactive learning approach and provide a discriminative model that is a 16-dimensional feature space comprising the textural appearance and contextual information. Dissimilarity measures in different neighborhood sizes are used to detect the difference within the neighborhood of an image pair. To detect changes between two images, the user designates change and non-change samples (pixel sets) in the images using a selection tool. This data is used to train a classifier using decision tree training method which is then applied to all the other pixels of the image pair. The experiments have proved the potential of the proposed approach.