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Outlier classification for microbiological open set recognition
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DOI：10.1016/j.compag.2024.109104.png)
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摘要
En 中文
Precise detection and effective intervention of pathogenic microorganisms are pivotal to ensure crop yield. Traditional open set recognition methods distinguish unknown classes, i.e., those not involved in training, as outliers or separate classes, which is insufficient for real -world applications like recognizing airborne microorganisms and particles. In this paper, a new setting for the classification of outliers for open set recognition is proposed. The proposed method classifies unknown classes while achieving the classification of known classes, treating each category in the open set equally. The two cores of the method are hierarchical feature representation and feature gallery construction. To enhance the feature representation, this paper proposes a novel architectural module of spatial attention on region hierarchy, namely SPARC. This module extracts features from hierarchical levels and models spatial interaction through the attention mechanism. With the induction of local -to -global description, minute differences with ambiguous appearances between classes can be detected, and the negative effects of image quality degradation can be ameliorated. Furthermore, a feature gallery containing the feature representation from both known and unknown classes is developed for feature retrieval. To demonstrate the effectiveness of the proposed model, the first dataset composed of agricultural microorganisms, named SMAD, is constructed, and experiments are conducted on existing datasets and the newly proposed SMAD dataset. The experimental results outperform the prior art on these datasets under both the outlier rejection and outlier classification settings.
Keyword:
Outlier classification
Open set recognition
Feature representation
Microorganism morphology recognition
期刊
IF:
8.9
论文数:
1.0W
被引数:
4.8W
机构
引用论文
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