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Similarity between Euclidean and cosine angle distance for nearest neighbor queries

9 years 9 months ago
Similarity between Euclidean and cosine angle distance for nearest neighbor queries
Understanding the relationship among different distance measures is helpful in choosing a proper one for a particular application. In this paper, we compare two commonly used distance measures in vector models, namely, Euclidean distance (EUD) and cosine angle distance (CAD), for nearest neighbor (NN) queries in high dimensional data spaces. Using theoretical analysis and experimental results, we show that the retrieval results based on EUD are similar to those based on CAD when dimension is high. We have applied CAD for content based image retrieval (CBIR). Retrieval results show that CAD works no worse than EUD, which is a commonly used distance measure for CBIR, while providing other advantages, such as naturally normalized distance. Keywords Vector model, Euclidean distance, Cosine angle distance, Content based image retrieval, Inter-feature normalization
Gang Qian, Shamik Sural, Yuelong Gu, Sakti Pramani
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where SAC
Authors Gang Qian, Shamik Sural, Yuelong Gu, Sakti Pramanik
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