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Fatma Imamoglu, Christof Koch, John-Dylan Haynes; MoonBase: Generating a database of two-tone "Mooney" images. Journal of Vision 2013;13(9):50. doi: 10.1167/13.9.50.
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© ARVO (1962-2015); The Authors (2016-present)
Thresholded two-tone ("Mooney") images are of interest for vision science because the object hidden within the image can be hard to recognize, with recognition times in the second to minute range. However, once a subject has seen the original grayscale image from which the Mooney is generated, recognition is much accelerated. Typically, suitable "Mooney" images need to be painstakingly generated by hand. Here, we present an approach for automatically generating a two-tone image database. This is based on large number of images collected from the internet. We first selected concrete words from a linguistic database. Using these words as search words, we automatically downloaded images from an online image database (www.flickr.com). Subsequently, the images were preprocessed and thresholded using a histogram based thresholding algorithm to generate the two-tone images. We provide an image set with 330 Mooney images and psychophysical results obtained from six subjects. With a presentation time of 20 s, the average recognition time was 9.36 s ± 7.40 s. Additionally, subjective ratings (confidence, Aha, and difficulty ratings) were obtained and are presented for each subject and image. This image set is, to our knowledge, the largest two-tone image set available to the vision and cognitive science research community (https://sites.google.com/site/hayneslab/links). We provide a Matlab toolbox that makes the extension of the image database possible. Using this toolbox, the researcher can add new object names as search words and create new two-tone images easily. Furthermore, we will present possibilities to extent this toolbox using another image database called ImageNet and introduce the use of Amazon Mechanical Turk to select useful images for a particular experiment. This image database can be useful for studying the mechanisms of rapid learning of visual image recognition, and has applications in research on conscious vision, learning, priming, reward and insight.
Meeting abstract presented at VSS 2013
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