Habitat image annotation with low-level features, medium-level knowledge and location information

The classification of habitats is crucial for structuring knowledge and developing our understanding of the natural world. Currently, most successful methods employ human surveyors—a laborious, expensive and subjective process. In this paper, we formulate habitat classification as a fine-grained visual categorization problem. We build on previous work and propose an image annotation framework that uses a novel automatic random forest-based method and that takes into consideration visual and geographical closeness in the classification process. During training, low-level visual features and medium-level contextual information are extracted. For the latter, we use a human-in-the-loop methodology by asking humans a set of 17 questions about the appearances of the image that can be easily answered by non-ecologists to extract medium-level knowledge about the images. During testing, and considering that close areas have similar ecological properties, we weigh the influence of the prediction of each tree of the forest according to their distance to the unseen test photography. Additionally, we present an updated version of a geo-referenced habitat image database containing over 1,000 high-resolution ground photographs that have been manually annotated by habitat classification experts. This has been made publicly available image database specifically designed for the development of multimedia analysis techniques for ecological applications. We show experimental recall and precision results which illustrate that our image annotation framework is able to annotate with a reasonable degree of confidence four of the main habitat classes: woodland and scrub, grassland and marsh, heathland and miscellaneous.