Symbolic Representation and Classification of Logos

TL;DR

This paper introduces a symbolic feature-based model for logo classification that effectively categorizes logos into text, symbol, or combined classes using clustering and symbolic interval data, demonstrating superior performance over existing methods.

Contribution

The paper presents a novel symbolic representation approach for logo classification that preserves intra-cluster variations and improves accuracy and efficiency compared to prior models.

Findings

Outperforms existing models in accuracy and F-measure.

Efficient classification with reduced time complexity.

Effective handling of intra-cluster variations using symbolic interval data.

Abstract

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