@inproceedings{a56911a27569407fb39e9be5f10d83e3,
title = "Rastlantisal karar aga{\c c}lariyla nesne ve renk dagilimina g{\"o}re sahne siniflandirilmasi",
abstract = "We propose a method to recognize the scene of an image by finding the objects and the colors it contains. We approach this problem by creating a binary vector of detected objects and a histogram of the colors that the image contains. We then use these features to train a random forest classifier in order to determine the scene of each image. For class-based classifiers, our method gives comparable results with the state of art methods, such as Object Bank method, for the indoor scene dataset that we used. Additionally, while well-known methods are computationally expensive, our method has a low computational cost.",
keywords = "Computer vision, Part based models, Random forests, Scene recognition",
author = "Ahmet Iscen and Eren G{\"o}lge and Anil Armagan and Pinar Duygulu",
year = "2013",
doi = "10.1109/SIU.2013.6531220",
language = "T{\"u}rk{\c c}e",
isbn = "9781467355629",
series = "2013 21st Signal Processing and Communications Applications Conference, SIU 2013",
booktitle = "2013 21st Signal Processing and Communications Applications Conference, SIU 2013",
note = "2013 21st Signal Processing and Communications Applications Conference, SIU 2013 ; Conference date: 24-04-2013 Through 26-04-2013",
}