# Fast Image Classification by Boosting Fuzzy Classifiers

Source: [Fast Image Classification by Boosting Fuzzy Classifiers](<https://www.sota2.com/research/paper/68fa62d41e209131ce9fb62e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:27:18.446Z

Authors: Marcin Korytkowski, Leszek Rutkowski, Rafa{\\l} Scherer

Published: 2016-10-04T16:18:57.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1610.01068>)

[PDF](<https://arxiv.org/pdf/1610.01068.pdf>)