Shape based leaf image retrieval

The authors present an efficient two-stage approach for leaf image retrieval by using simple shape features including centroid-contour distance (CCD) curve, eccentricity and angle code histogram (ACH). In the first stage, the images that are dissimilar with the query image are first filtered out by using eccentricity to reduce the search space, and fine retrieval follows by using all three sets of features in the reduced search space in the second stage. Different from eccentricity and ACH, the CCD curve is neither scaling-invariant nor rotation-invariant. Therefore, normalisation is required for the CCD curve to achieve scaling invariance, and starting point location is required to achieve rotation invariance with the similarity measure of CCD curves. A thinning-based method is proposed to locate starting points of leaf image contours, so that the approach used is more computationally efficient. Actually, the method can benefit other shape representations that are sensitive to starting points by reducing the matching time in image recognition and retrieval. Experimental results on 1400 leaf images from 140 plants show that the proposed approach can achieve a better retrieval performance than both the curvature scale space (CSS) method and the modified Fourier descriptor (MFD) method. In addition, the two-stage approach can achieve a performance comparable to an exhaustive search, but with a much reduced computational complexity.