mCLOVER: mobile content-based leaf image retrieval system
Proceedings of the 13th annual ACM international conference on Multimedia
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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
The VLDB Journal — The International Journal on Very Large Data Bases
Shape-based indexing scheme for camera view invariant 3-D object retrieval
Multimedia Tools and Applications
PDA plant search system based on the characteristics of leaves using fuzzy function
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Scaling-invariant boundary image matching using time-series matching techniques
Data & Knowledge Engineering
A parametric active polygon for leaf segmentation and shape estimation
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
A venation-based leaf image classification scheme
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Feature extraction and XML representation of plant leaf for image retrieval
APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
A shape-based retrieval scheme for leaf images
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
Understanding leaves in natural images - A model-based approach for tree species identification
Computer Vision and Image Understanding
Content-based diversifying leaf image retrieval
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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In this paper we present an efficient two-step approach of using a shape characterization function called centroid-contour distance curve and the object eccentricity (or elongation) for leaf image retrieval. Both the centroid-contour distance curve and the eccentricity of a leaf image are scale, rotation, and translation invariant after proper normalizations. In the frist step, the eccentricity is used to rank leaf images, and the top scored images are further ranked using the centroid-contour distance curve together with the eccentricity in the second step. A thinning-based method is used to locate start point(s) for reducing the matching time. Experimental results show that our approach can achieve good performance with a reasonable computational complexity.