Homogeneity has same or similar shape is so common in the abstract and in nature, and shape similarity is a very important factor for classification and object recognition. Traditionally, a multidimensional vector is treated as a point of the feature space, we calculate the distance between the points to measure the similarity, the smaller the distance, the greater the similarity. The popular similarity measures maybe the Manhattan and Euclidean distances. In this paper, we showed the Minkowski metric computed by the absolute difference of vectors, and ignored the characteristic of the differences. According our previous works, we used objects but not points to respect the vector in the feature space, then the shape similarity can be respected by the character of the differences between vectors. Based on this point, a quasimetric distance was used for similarity estimation. Experiment results on two benchmark datasets from the UCI repository showed this kind of distance can achieve higher accuracy than the classical Manhattan and Euclidean distances in similarity estimation.

Date Published: 10 July 2009
PDF: 8 pages
Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 74890C (10 July 2009); doi: 10.1117/12.836386

Published in SPIE Proceedings Vol. 7489:
PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering
Honghua Tan; Qi Luo, Editor(s)