A shape-based retrieval scheme for leaf images

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.