Yazar
Maxudov, Nekruzjon, Özcan, Barış, Kıraç, Mustafa Furkan
Basım Tarihi
2016
Konu
Descriptors, Scene recognition, Bag of words, SIFT, SURF
Tür
Belge
Dil
İngilizce
Dijital
Evet
Yazma
Hayır
Kütüphane
Özyeğin Üniversitesi
Demirbaş Numarası
978-1-5090-1679-2
Kayıt Numarası
98c24e4b-e632-413e-8826-7d34b80e1f52
Lokasyon
Computer Science
Tarih
2016
Örnek Metin
In 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.
DOI
10.1109/SIU.2016.7496070
