Jaccard distance based weighted sparse representation for coarse-to-fine plant species recognition

Abstract

Leaf based plant species recognition plays an important role in ecological protection, however its application to large and modern leaf databases has been a long-standing obstacle due to the computational cost and feasibility. Recognizing such limitations, we propose a Jaccard distance based sparse representation (JDSR) method which adopts a two-stage, coarse to fine strategy for plant species recognition. In the first stage, we use the Jaccard distance between the test sample and each training sample to coarsely determine the candidate classes of the test sample. The second stage includes a Jaccard distance based weighted sparse representation based classification(WSRC), which aims to approximately represent the test sample in the training space, and classify it by the approximation residuals. Since the training model of our JDSR method involves much fewer but more informative representatives, this method is expected to overcome the limitation of high computational and memory costs in traditional sparse representation based classification. Comparative experimental results on a public leaf image database demonstrate that the proposed method outperforms other existing feature extraction and SRC based plant recognition methods in terms of both accuracy and computational speed.

Happy Diamond Anniversary JMS! A Decade Analysis of the Journal of Management Studies

“…Our main explanatory variables focus on several dimensions of diversity (internationality, interdisciplinarity, and gender) under the general assumption that more diverse teams of co‐authors will produce more impactful work (as proxied by subsequent citations). To derive measures for knowledge (i.e., interdisciplinarity ) and international diversity within a team of co‐authors we rely on modified Jaccard indexes, which have been employed to capture dissimilarity (or distance) between different sets of characteristics (Krammer, 2016; Zhang et al., 2017). Specifically, for capturing interdisciplinarity we examine prior publication records of all co‐authors in a team and look at the distribution of their prior publications across 22 management sub‐disciplines (as classified in the CABS’ Academic Journal Guide (AJG) 2021 list).…”