Scene classification with random forests and object and color distributions
Author
İşcen, Ahmet
Gölge, Eren
Armağan, Anıl
Duygulu, Pınar
Date
2013Source Title
2013 21st Signal Processing and Communications Applications Conference (SIU)
Publisher
IEEE
Language
Turkish
Type
Conference PaperItem Usage Stats
167
views
views
119
downloads
downloads
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. © 2013 IEEE.
Keywords
Computer visionPart based models
Random forests
Scene recognition
Color distribution
Computational costs
Part-based models
Random forest classifier
Random forests
Scene classification
Scene recognition
State-of-art methods
Color
Computer vision
Signal processing
Decision trees