Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
Journal of the ACM (JACM)
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Plant Leaf Identification Using Multi-scale Fractal Dimension
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Plant texture classification using gabor co-occurrences
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Leaf recognition based on the combination of wavelet transform and gaussian interpolation
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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A method is presented for the classification of images described using high-dimensional probability density functions (pdfs). A pdf is described by a set of n points sampled from its distribution. These points represent feature vectors calculated from windows sampled from an image. A mapping is found, using the Hungarian algorithm, between the set of points describing a class, and the set for a pdf to be classified, such that the distance that points must be moved to change one set into the other is minimized. The method uses these mappings to create a classifier that can model the variation within each class. The method is applied to the problem of classifying plants based on images of their leaves, and is found to outperform several existing methods.