Author
Maxudov, Nekruzjon, Özcan, Barış, Kıraç, Mustafa Furkan
Publication Date
2016
Subject
Descriptors, Scene recognition, Bag of words, SIFT, SURF
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-1-5090-1679-2
Record ID
98c24e4b-e632-413e-8826-7d34b80e1f52
Library Location
Computer Science
Date
2016
Sample Text
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
