An Image Classification Based on New Similarity Measure in Rough Sets
Takehiko Matsuura, Syoji Kobashi, Katsuya Kondo, Yutaka Hata
Proc. of 1st Int. Conf. on Fuzzy Systems and Knowledge Discovery,
CD-ROM, (2002)
This paper proposes an image classification method using 'value reduct,'
which is a concept of rough sets. In this method, attribute values of
each pixels of an image are given by using K-means clustering, and the
attribute values divide the image into many regions. By applying the
value reduct to the attribute values, dissimilarities between regions
are calculated. Dissimilarities between regions make initial equivalence
relations. Similarities between regions are calculated based on the
relations. Finally, this method forms final equivalence relation between
regions based on similarities, and classifies them into some regions
according to the relations. The performance of the algorithm was evaluated
by applying it to an artificial generated image and a human brain
MR (magnetic resonance) slice image.