2013•arXiv (Cornell University)Open access

Experiments of Distance Measurements in a Foliage Plant Retrieval System

Abdul Kadir, Lukito Edi Nugroho, Adhi Susanto, Paulus Insap Santosa

Abstract

One of important components in an image retrieval system is selecting a distance measure to compute rank between two objects. In this paper, several distance measures were researched to implement a foliage plant retrieval system. Sixty kinds of foliage plants with various leaf color and shape were used to test the performance of 7 different kinds of distance measures: city block distance, Euclidean distance, Canberra distance, Bray-Curtis distance, x2 statistics, Jensen Shannon divergence and Kullback Leibler divergence. The results show that city block and Euclidean distance measures gave the best performance among the others.

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Key concepts: Euclidean distance, Distance measures, Divergence (linguistics), Mathematics, City block, Minimum distance, Block (permutation group theory), Rank (graph theory)