Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indexing Flower Patent Images Using Domain Knowledge
IEEE Intelligent Systems
A Visual Vocabulary for Flower Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Utilizing venation features for efficient leaf image retrieval
Journal of Systems and Software
A similarity-based leaf image retrieval scheme: Joining shape and venation features
Computer Vision and Image Understanding
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Review: Plant species identification using digital morphometrics: A review
Expert Systems with Applications: An International Journal
Advanced shape context for plant species identification using leaf image retrieval
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Extraction of leaf parts by image analysis
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Leaf Shape Descriptor for Tree Species Identification
ICME '12 Proceedings of the 2012 IEEE International Conference on Multimedia and Expo
Multi-organ plant identification
Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data
Leafsnap: a computer vision system for automatic plant species identification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
KOKOPIN app: a mobile platform for biogeography
Proceedings of the 2nd ACM international workshop on Multimedia analysis for ecological data
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Automatic retrieval tools are becoming increasingly important in botany and agriculture due to the growing interest in biodiversity and the ongoing shortage of skilled taxonomists. Our work is motivated by a botanical field scenario where the basic unit of observation is a plant. We describe a novel, image-based retrieval system for both educational and decision-making purposes. Given multiple leaf images of the same plant, the algorithm displays a ranked list of the most relevant species, along with a varied set of representative images from each estimated species. We focus on leaves but the strategy is generic, based on a hierarchical representation of latent variables called identification keys (IdKeys) which embody domain knowledge about taxonomy and landmarks. For each query image, keys are estimated sequentially, proceeding from landmarks to the genus and finally to an estimated set of species. The results over multiple queries are then collated into a single ranked list of species. Experiments demonstrate that the proposed approach achieves excellent performance on several databases of uncluttered leaf images as well as providing an instructive interface for measuring diversity and identifying new species.