Aptoula,E.2024-05-252024-05-252014978-147993990-91949-399110.1109/CBMI.2014.68498372-s2.0-84904962570https://doi.org/10.1109/CBMI.2014.6849837an der Universitat Klagenfurt (FTF); Forderverein Technische Fakultat; Industriellen Vereinigung (IV); mediamidPlaced in the context of geographical content-based image retrieval, in this paper we explore the description potential of morphological texture descriptors when combined with the popular bag-of-visual-words paradigm. In particular, we adapt existing global morphological texture descriptors, so that they are computed within local sub-windows and then form a vocabulary of 'visual morphological words' through clustering. The resulting image features, are thus visual word histograms and are evaluated using the UC Merced Land Use-Land Cover dataset. Moreover, the local approach under study is compared against alternative global and local descriptors across a variety of settings. Despite being one of the initial attempts at localized morphological content description, the retrieval scores indicate that vocabulary based morphological content description possesses a significant discriminatory potential. © 2014 IEEE.eninfo:eu-repo/semantics/closedAccess[No Keyword Available]Conference Object19