Yanikoglu,B.Aptoula,E.Yildiran,S.T.2024-10-152024-10-15201301613-0073[SCOPUS-DOI-BELIRLENECEK-131]2-s2.0-84922021339https://hdl.handle.net/20.500.14517/6761We describe our participation in the plant identification task of ImageClef 2013. We submitted one fully automatic run that uses different features for the uniform background (isolated leaves) and natural background (unconstrained photos) categories. Besides the category information, meta-data was only used in the natural background category. Our approach employs a variety of shape, texture and color descriptors. As in the previous years, we used shape and texture only for isolated leaves and observed them to be very effective. Our system obtained the best results in this category with a score of 0.607 which is the inverse rank of the retrieved class, averaged over all queried photos and users. As for the natural background category, we used a limited approach using a restricted set of features that were extracted globally due to lack of time, and obtained a score of 0.181.eninfo:eu-repo/semantics/closedAccessMathematical morphologyPlant identificationSupport vector machinesConference ObjectQ41179