Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Graph kernels between point clouds
Proceedings of the 25th international conference on Machine learning
Efficiently matching sets of features with random histograms
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A posteriori multi-probe locality sensitive hashing
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Performance evaluation of local colour invariants
Computer Vision and Image Understanding
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
International Journal of Computer Vision
Automated Flower Classification over a Large Number of Classes
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Interactive objects retrieval with efficient boosting
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Shape-based image retrieval in botanical collections
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
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In the context of computer-assisted plant identification we are facing challenging information retrieval problems because of the very high within-class variability and of the limited number of training examples. To address these problems, we suggest a new interactive learning approach that combines similarity-based retrieval and re-ranking by SVM using local feature distributions. This approach leads to improved sample selection, allowing to obtain better results.