Aptoula, Erchan2024-10-152024-10-152014197814799399091949-3983[WOS-DOI-BELIRLENECEK-183]https://hdl.handle.net/20.500.14517/6361Placed 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.eninfo:eu-repo/semantics/closedAccess[No Keyword Available]Conference ObjectWOS:000358799400024