Abstract
This contribution presents a system for detection of similar images of advertisements in moderate size datasets. These datasets are daily updated and mainly consists of advertisements from tv, newspapers, journals, etc. The task is to identify clusters of duplicate advertisements in given dataset. Images differ by translation, scale and the amount of compression. The presented approach is based on recently popular bag-of-features approach which has been successfully used in context of image retrieval and other related areas. Each image is represented as weighted histogram of local features. Similarities are computed based on the extracted features are projected onto separating hyperplane and clustered using agglomerative hierarchical clustering. Experiments show that this simple and efficient scheme yields good results and finds corresponding images even for advertisements which are substantially dissimilar in spatial arrangement and color composition with reasonable false positive rate.
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Palecek, K. (2011). Detection of Similar Advertisements in Media Databases. In: Esposito, A., Vinciarelli, A., Vicsi, K., Pelachaud, C., Nijholt, A. (eds) Analysis of Verbal and Nonverbal Communication and Enactment. The Processing Issues. Lecture Notes in Computer Science, vol 6800. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25775-9_18
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DOI: https://doi.org/10.1007/978-3-642-25775-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25774-2
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