Features Fusion for Classification of Logos

TL;DR

This paper presents a logo classification system that uses combined global features like color, texture, and shape, achieving improved accuracy through feature fusion and a custom dataset of over 5000 images.

Contribution

It introduces a novel logo classification approach utilizing feature fusion of color, texture, and shape with a new dataset and evaluates performance with multiple metrics.

Findings

Feature fusion improves classification accuracy

Color logo dataset of 5044 images created for research

Fusion of all three features yields the best results

Abstract

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.