Softmax函数
计算机科学
模式识别(心理学)
乙状窦函数
相似性(几何)
人工智能
布线(电子设计自动化)
欧几里德距离
特征(语言学)
数据挖掘
图像(数学)
卷积神经网络
人工神经网络
计算机网络
语言学
哲学
作者
Patrick Mensah,Benjamin Asubam Weyori,Mighty Abra Ayidzoe
出处
期刊:Journal of Intelligent and Fuzzy Systems
[IOS Press]
日期:2021-01-01
卷期号:40 (1): 1025-1036
被引量:4
摘要
Capsule Networks (CapsNets) excel on simple image recognition problems. However, they fail to perform on complex images with high similarity and background objects. This paper proposes Local Binary Pattern (LBP) k-means routing and evaluates its performance on three publicly available plant disease datasets containing images with high similarity and background objects. The proposed routing algorithm adopts the squared Euclidean distance, sigmoid function, and a ‘simple-squash’ in place of dot product, SoftMax normalizer, and the squashing function found respectively in the dynamic routing algorithm. Extensive experiments conducted on the three datasets showed that the proposed model achieves consistent improvement in test accuracy across the three datasets as well as allowing an increase in the number of routing iterations with no performance degradation. The proposed model outperformed a baseline CapsNet by 8.37% on the tomato dataset with an overall test accuracy of 98.80%, comparable to state-of-the-art models on the same datasets.
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