Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Digital Image Processing
Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Leaf Image Retrieval with Shape Features
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Skeleton Based Shape Matching and Retrieval
SMI '03 Proceedings of the Shape Modeling International 2003
Matching shapes with self-intersections: application to leaf classification
IEEE Transactions on Image Processing
Fractal dimension applied to plant identification
Information Sciences: an International Journal
A venation-based leaf image classification scheme
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
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Content-based image retrieval (CBIR) usually utilizes image features such as color, shape, and texture. For good retrieval performance, appropriate object features should be selected, well represented and efficiently evaluated for matching. If images have similar color or texture like leaves, shape-based image retrieval could be more effective than retrieval using color or texture. In this paper, we present an effective and robust leaf image retrieval system based on shape feature. For the shape representation, we revised the MPP algorithm in order to reduce the number of points to consider. Moreover, to improve the matching time, we proposed a new dynamic matching algorithm based on the Nearest Neighbor search method. We implemented a prototype system and performed various experiments to show its effectiveness. Its performance is compared with other methods including Centroid Contour Distance (CCD), Fourier Descriptor, Curvature Scale Space Descriptor (CSSD), Moment Invariants, and MPP. Experimental results on one thousand leaf images show that our approach achieves a better performance than other methods.