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An Analysis on Visual Recognizability of Onomatopoeia Using Web Images and DCNN features Wataru

In this paper, we examine the relation between onomatopoeia and images using a large number of images over the Web. The objective of this paper is to examine if the images corresponding to Japanese onomatopoeia words which express the feeling of visual appearance can be recognized by the state-of-theart visual recognition methods. In our work, first, we collect the images corresponding to onomatopoeia words using an Web image search engine, and then we filter out noise images to obtain clean dataset with automatic image re-ranking method. Next, we analyze recognizability of various kinds of onomatopoeia images by using improved Fisher vector (IFV) and deep convolutional neural network (DCNN) features. By the experiments, it has been shown that the DCNN features extracted from the layer 5 of Overfeat’s network pre-trained with the ILSVRC 2013 data have prominent ability to represent onomatopoeia images.