2017 年 32 巻 1 号 p. WII-N_1-10
Image database is one of the important research topics in image recognition. A manual collection of images would causes biased collection and a lot of human efforts. Huge image database where varied and many unbiased objects are stored is required for the image learning. Recent researches for image database try to automatically/semiautomatically generate image database. Here, it is important to remove noise images and this paper proposes an automatic generation method of the Web image database. The proposed method uses noise image removal by visual feature and semantic feature in a hybrid. However, which type of features, and how to combine the two types of feature are not clear and should be investigated. In this paper, six kinds 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 in experiments, it was confirmed that the method using both visual and semantic features in parallel focusing on noise images showed over 82% Precision values，76% Recall values and 77% Fmeasure values in average. Also, the usability of the generated database for image recognition was confirmed through the experiments; It was equal to or higher than the human-made database. It was confirmed that the proposed method constructed precise image database full-automatically.