A New Database and Protocol for Image Reuse Detection
The use of visual elements of an existing image while creating new ones is a commonly observed phenomenon in digital artworks. The practice, which is referred to as image reuse, is not an easy one to detect even with the human eye, less so using computati
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Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA [email protected] Department of Computer Engineering, Bo˘ gazi¸ci University, Istanbul, Turkey [email protected], [email protected] 3 ˙ College of Communication, Istanbul S ¸ ehir University, Istanbul, Turkey [email protected]
Abstract. The use of visual elements of an existing image while creating new ones is a commonly observed phenomenon in digital artworks. The practice, which is referred to as image reuse, is not an easy one to detect even with the human eye, less so using computational methods. In this paper, we study the automatic image reuse detection in digital artworks as an image retrieval problem. First, we introduce a new digital art database (BODAIR) that consists of a set of digital artworks that re-use stock images. Then, we evaluate a set of existing image descriptors for image reuse detection, providing a baseline for the detection of image reuse in digital artworks. Finally, we propose an image retrieval method tailored for reuse detection, by combining saliency maps with the image descriptors. Keywords: Image database · Digital art · Image retrieval extraction · DeviantArt · Image reuse · BODAIR
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Introduction
One of the main focus of art historical research is the detection of stylistic similarities between works of art. As early as 1915, Heinrich W¨ ollflin, who is deemed by many as the “father” of art history as a discipline, introduced the notion of comparing artworks to define the style of a period [1]. Art historians, connoisseurs, and art critics are trained to detect whether certain features of an artwork are apparent in another one, and whether two artworks belong to the same artist or not. The experts not only use their visual understanding for such detection, but also rely heavily on historical records and archival information, which are not always sufficiently clear or available. Hence, for decades, art historical research has applied scientific methods such as infrared and x-ray photographic techniques (among others) to help in different instances where the c Springer International Publishing Switzerland 2016 G. Hua and H. J´ egou (Eds.): ECCV 2016 Workshops, Part I, LNCS 9913, pp. 903–916, 2016. DOI: 10.1007/978-3-319-46604-0 62
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trained eye faltered. Using computational approaches in detecting stylistic traditions of artworks is a relatively new addition to the field [2]. In this paper, we introduce a new digital image database that consists of original artworks that are re-used to create new artworks. We use this database to examine approaches for image reuse detection. In the long run, trying to detect which image is reused with computational methods will help in detecting stylistic similarities between artworks in general [3]. In Western tradition, artists learned their trade by joining ateliers of masters as apprentices. With the introduction of printing press and the wider availability of paper, and especially due to the
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