Multi-grained Fusion Image Feature Learning with Fuzzy Rule System

Abstract

Currently, the most popular image feature learning method is deep neural network, which can automatically extract efficient features through feature learning for tasks such as classification and recognition without human involvement. However, the deep neural network image feature extraction method currently faces many challenges. Its effectiveness relies heavily on large data, and it is usually regarded as a black box model with poor interpretability. Aiming at the above challenges, a more interpretable and scalable feature learning method, multi-grained fusion image feature learning with fuzzy rule system, is proposed in the paper. The method is based on Takagi-Sugeno-Kang fuzzy system (TSK-FS) with fuzzy rule inference. This method extracts image features through rule-based TSK-FS, so the feature learning process can be explained by rules. Then, multi-granularity scanning also further enhances its feature learning ability. Extensive experiments have been conducted on image datasets of different scales, and the results show the proposed method is effective on image datasets.

Keywords

• feature learning
• fuzzy system
• non-linear model
• image classification
• interpretability