A comparative analysis of two distance measures in color image databases

Euclidean distance measure has been used in comparing feature vectors of images, while cosine angle distance measure is used in document retrieval. In this paper, we theoretically analyze these two distance measures based on feature vectors normalized by image size and experiment with them in the context of color image database. We find that the cosine angle distance, in general, works equally well for image databases. We show, for a given query vector, characteristics of feature vectors that will be favored by one measure but not by the other. We compute k-nearest neighbors for query images using both Euclidean and cosine angle distance for a small image database. The experimental data corroborate our theoretical results.

• Type: Conference
• Year: 2002
• Where: ICIP
• Authors: Shamik Sural, Gang Qian, Sakti Pramanik
• Keywords: Angle Distance Measure, Cosine Angle Distance, Euclidean Distance Measure, ICIP 2002, Image Processing