Maxudov, NekruzjonÖzcan, BarışKıraç, Mustafa Furkan2016-09-182016-09-182016978-1-5090-1679-2http://hdl.handle.net/10679/4471https://doi.org/10.1109/SIU.2016.7496070In this paper, scene recognition problem, which is a frequently-studied field of computer vision, is tackled. Proposed algorithm utilizes bag of words (BoW) method along with considering sub-segments in the image during classification. For this purpose, the image is represented in three sub-segment levels where the image is divided into equal sized sub-segments at each level. The number of sub-segments are increased as the sub-segment level is increased and each sub-segment at each level is classified. During classification, responses of different sub-segment levels to classifier is considered with a major voting policy. The experiments are made on a database that contains approximately 4500 samples of scene images with dictionary sizes of 50, 100, 200, 300 and different sub-segment levels. The results show that, the proposed method achieves 71.83% accuracy and the sub-segment major voting increases the performance by % 1 according to the non-major voting case.engrestrictedAccessconferenceObject00039125090038610.1109/SIU.2016.7496070DescriptorsScene recognitionBag of wordsSIFTSURF2-s2.0-84982854032