Alt kesit seviyeleri arasında oy çoğunluğu ile sahne tanıma

Name: Alt kesit seviyeleri arasında oy çoğunluğu ile sahne tanıma
Author: Maxudov, Nekruzjon, Özcan, Barış, Kıraç, Mustafa Furkan

İsim	Alt kesit seviyeleri arasında oy çoğunluğu ile sahne tanıma
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

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Alt kesit seviyeleri arasında oy çoğunluğu ile sahne tanıma

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