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In this paper, a model for classification of logos based on symbolicrepresentation of features is presented. The proposed model makes use of globalfeatures of logo images such as color, texture, and shape features forclassification. The logo images are broadly classified into three differentclasses, viz., logo image containing only text, an image with only symbol, andan image with both text and a symbol. In each class, the similar looking logoimages are clustered using K-means clustering algorithm. The intra-clustervariations present in each cluster corresponding to each class are thenpreserved using symbolic interval data. Thus referenced logo images arerepresented in the form of interval data. A sample logo image is thenclassified using suitable symbolic classifier. For experimentation purpose,relatively large amount of color logo images is created consisting of 5044 logoimages. The classification results are validated with the help of accuracy,precision, recall, F-measure, and time. To check the efficacy of the proposedmodel, the comparative analyses are given against the other models. The resultsshow that the proposed model outperforms the other models with respect to timeand F-measure.

Symbolic Representation and Classification of Logos