Recognition of leaf image set based on manifold–manifold distance

Recognizing plant leaves has been a difficult and important work. In this paper, we formulate the problems by classifying leaf image sets rather than single-shot image, each of set contains leaf images pertaining to the same class. We extract leaf image feature and compute the distance between two manifolds modeled by leaf images. Specifically, we apply a clustering procedure in order to express a manifold by a collection of local linear models. Then the distance is measured between local models which come from different manifolds that constructed above. Finally, the problem is transformed to integrate the distance between pairs of subspace. Experiment based on the leaves (ICL) from intelligent computing laboratory of Chinese academy of sciences, which shows that the method has a great performance.