Well-Known brands recognition by automated classifiers using local and global features

Abstract

From color and type to patterns and illustrations, brands sense to be recognizable and convey their values and personality. Here patterns and color are key elements, as they can play a vital role in brand recognition. The images used for brand classification were handpicked and collectively named as HKDataset. We have explored various feature extractors used for classification and used automated classifiers named Linear SVM to achieve higher accuracy while tuning the model parameters to achieve optimal performance. It has been observed that Support Vector Machines performs better when using GIST descriptors combined with Bag of SIFT features. We hope to apply deep learning and other sophisticated classifiers to much-expanded categories of brands in the future.