Paper Info
Title
A practical classifier for photographs and non-photographic images based on local visual features
Abstract
Classification of digital images into photographs and various kinds of non-photographic images has not been sufficiently studied but has many applications such as retrieval of real scene photographs from web sites and image databases. In this paper, we show that the combination of Bag of Visual Words of SURF features and histograms of LBPs for HSV and Luminance components (SURF+LBP(HSVL)) is simple, but works well as visual features for photographs and non-photographic image classification. We found that a classifier trained with SURF+LBP(HSVL) was the best among all the classifiers we tested using various visual features. Our classifier attained an accuracy of 96.8% for our image dataset and outperformed the other state-of-the-art classifiers.
Year
DOI
Venue
2015
10.1109/MVA.2015.7153192
2015 14th IAPR International Conference on Machine Vision Applications (MVA)
Keywords
Field
DocType
photograph image classifier,local visual features,digital image classification,Web site,image database,bag of visual words,SURF features,LBP histograms,HSV,luminance component,nonphotographic image classification,classifier training,image dataset
Histogram,Computer vision,Pattern recognition,Bag-of-words model in computer vision,Visualization,Computer science,Support vector machine,Digital image,Artificial intelligence,Contextual image classification,Classifier (linguistics),Luminance
Conference
Citations 
PageRank 
References 
0
0.34
10
Authors
2
Name
Order
Citations
PageRank
Kei Terayama100.68
Hirohisa Hioki202.37