  In this paper, a logo classification system based on the appearance of logo
images is proposed. The proposed classification system makes use of global
characteristics of logo images for classification. Color, texture, and shape of
a logo wholly describe the global characteristics of logo images. The various
combinations of these characteristics are used for classification. The
combination contains only with single feature or with fusion of two features or
fusion of all three features considered at a time respectively. Further, the
system categorizes the logo image into: a logo image with fully text or with
fully symbols or containing both symbols and texts.. The K-Nearest Neighbour
(K-NN) classifier is used for classification. Due to the lack of color logo
image dataset in the literature, the same is created consisting 5044 color logo
images. Finally, the performance of the classification system is evaluated
through accuracy, precision, recall and F-measure computed from the confusion
matrix. The experimental results show that the most promising results are
obtained for fusion of features.
