Flower classification for a citizen science mobile app

This work describes an efficient approach for flower classification that is suitable for deployment in mobile devices, allowing its use in a citizen science application for biodiversity monitoring. In the proposed system, geo-located images are uploaded by the user and segmented semi-automatically. We propose a classification method based on histogram comparison of color, shape and texture cues, using metric learning for feature weighting. Our method is tested on the Oxford Flower Dataset and we are able to achieve state-of-the-art accuracy, while proposing an approach that can run efficiently in mobile devices. Copyright 2014 ACM.