Machine Learning
Random Forests for land cover classification
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structured class-labels in random forests for semantic image labelling
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Strong supervision from weak annotation: Interactive training of deformable part models
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Multimedia analysis for ecological data
Proceedings of the 20th ACM international conference on Multimedia
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Habitat classification is important for monitoring the environment and biodiversity. Currently, this is done manually by human surveyors, a laborious, expensive and subjective process. We have developed a new computer habitat classification method based on automatically tagging geo-referenced ground photographs. In this paper, we present a geo-referenced habitat image database containing over 400 high-resolution ground photographs that have been manually annotated by experts based on a hierarchical habitat classification scheme widely used by ecologists. This will be the first publicly available image database specifically designed for the development of multimedia analysis techniques for ecological (habitat classification) applications. We formulate photograph-based habitat classification as an automatic image tagging problem and we have developed a novel random-forest based method for annotating an image with the habitat categories it contains. We have also developed an efficient and fast random-projection based technique for constructing the random forest. We present experimental results to show that ground-taken photographs are a potential source of information that can be exploited in automatic habitat classification and that our approach is able to classify with a reasonable degree of confidence three of the main habitat classes: Woodland and Scrub, Grassland and Marsh and Miscellaneous.