Abstract
Web-scale databases and big data collections are computationally challenging to analyze and search. Similarity or more precisely nearest neighbor searches are thus crucial in the analysis, indexing and utilization of these massive multimedia databases. In this work, we begin by reviewing the top approaches from the research literature in the past decade. Furthermore, we evaluate the scalability and computational complexity as the feature complexity and database size vary. For the experiments, we used two different data sets with different dimensionalities. The results reveal interesting insights regarding the index structures and their behavior when the data set size is increased. We also summarized the ideas, strategies and challenges for the future.
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