Effectiveness Comparison of Visual and Semantic Features for Noise Image Removal

This paper describes the effectiveness comparison of noise removal method using visual, semantic and the both features. To automatically generate image dataset from Web, noise image removal should be conducted. Visual and semantic features are available to detect noise images. However, which type of features, and how to combine the two types of feature are unraveled. In this paper, six types of noise image detection method are prepared: the method using visual feature, the method using semantic feature, two methods using both features in parallel and two methods using both features in serial. Through the comparison experiments, it was confirmed that the method that used both visual and semantic features in parallel focusing on noise images: the method showed 77.5% F-measure values. The image dataset with the method would be applied into image recognition in our future. c © 2016 The Authors. Published by Elsevier B.V. Peer-review under responsibility of KES International.