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Feedback Based Evolutionary Spiral Learning Method for Reducing Class Ambiguity
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DOI：10.1109/ACCESS.2024.3442205.png)
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Abstract
En 中文
In recent years, the deep learning based image classification has emerged as a crucial research topic in the fields of computer vision and artificial intelligence. However, the ambiguity inherent in image classification tasks causes the reduced accuracy in the classification process due to similarities between classes. This study proposes a feedback based evolutionary spiral learning method for reducing class ambiguity in the deep learning based image classification process. The proposed method consists of four stages: data collection, key image clustering, classification model training, and evaluation of classification results. If the evaluation results converge to specific measured values, the corresponding key image clusters are used as training data; otherwise, the process iterates through the previous stages in a spiral structure. Various experiments showed that the proposed method, compared to the traditional approach of manually labeling and generating training data, highly improved performance with an average of 82.38%. This reveals that the method can become a significant solution to address the issue of ambiguity that arises in deep learning based image classification tasks. In addition, it is expected to provide a faster and more optimized process in the fields of multi-class classification and detection.
Keywords:
Training
Image classification
Computational modeling
Deep learning
Transformers
Spirals
Feature extraction
Learning systems
Learning method
class ambiguity
spiral model
image classification
object detection