Abstract
Zoomed-in image retrieval is a special field of near-duplicate image retrieval. It allows determining the sight, panorama or landscape image where a zoomed-in image belongs to. In addition, it can help to detect copyright violations of images that have been cropped and rescaled from panorama images. So far, only few research have been done on this problem using supervised learning techniques. We present a method to retrieve and localize zoomed-in images with respect to the whole scene based on correlating groups of features. Feature grouping is used to filter features that do not contribute to identifying relations between images. The remaining features are used to estimate the scale and location of the zoomed-in image with respect to the whole scene. We provide results of a benchmark data study using the proposed method to detect zoomed-in images and to localize them in the correlating whole scene images. We compare our method with the RANSAC model in case of zoomed-in retrieval and localization. The results indicate that our approach is more robust than the RANSAC model and can detect the relation and localize zoomed-in images even when most matched features are not correlated or only a few matches can be found.
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Ahmad Alyosef, A., Nürnberger, A. (2019). Detecting Sub-Image Replicas: Retrieval and Localization of Zoomed-In Images. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_23
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DOI: https://doi.org/10.1007/978-3-030-29891-3_23
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