Fast Image Classification by Boosting Fuzzy Classifiers

TL;DR

This paper introduces a new fuzzy classifier boosting method for image classification that improves accuracy and reduces learning and testing time compared to traditional bag-of-features models with SVM.

Contribution

It proposes a novel boosting-based fuzzy classifier approach that leverages local image features for efficient and accurate object classification.

Findings

Achieves higher classification accuracy than traditional methods.

Reduces learning and testing time by over 30%.

Outperforms bag-of-features + SVM approach.

Abstract

This paper presents a novel approach to visual objects classification based on generating simple fuzzy classifiers using local image features to distinguish between one known class and other classes. Boosting meta learning is used to find the most representative local features. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives better classification accuracy and the time of learning and testing process is more than 30% shorter.