Abstract
As the volume of non-textual data, such images and other multimedia data, available on Internet is increasing. The issue of identifying data items based on query containment rather than query equality is becoming more and more important. In this paper, we propose a solution to this problem. We assume local descriptors are extracted from data items, so the aforementioned problem reduces to finding data items that share as many as possible local descriptors with the query. In particular, we define a new ε-intersection for this purpose. Local descriptors usually contain the location of the descriptors, so the proposed solution takes them into account to increase effectiveness of searching. We evaluate the ε-intersection on two real-life image collections using SIFT and SURF local descriptors from both effectiveness and efficiency points of view. Moreover, we study the influence of individual parameters of the ε-intersection to query results.
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Homola, T., Dohnal, V., Zezula, P. (2011). Proximity-Based Order-Respecting Intersection for Searching in Image Databases. In: Detyniecki, M., Knees, P., Nürnberger, A., Schedl, M., Stober, S. (eds) Adaptive Multimedia Retrieval. Context, Exploration, and Fusion. AMR 2010. Lecture Notes in Computer Science, vol 6817. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27169-4_13
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DOI: https://doi.org/10.1007/978-3-642-27169-4_13
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