Building a scalable and accurate copy detection mechanism
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In this paper we provide a comparative study of content-based copy detection methods, which include research literature methods based on salient point matching (SURF), discrete cosine and wavelet transforms, color histograms, biologically motivated visual matching and other methods. In our evaluation we focus on large-scale applications, especially on performance in the context of search engines for web images. We assess the scalability of the tested methods by investigating the detection accuracy relative to descriptor size, description time per image and matching time per image. For testing, original images altered by a diverse set of realistic transformations are embedded in a collection of one million web images.