Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
391839 | Information Sciences | 2016 | 8 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Marcin Korytkowski, Leszek Rutkowski, RafaĆ Scherer,