Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis
This work addresses a specific issue in neural network architectures for agricultural image analysis, offering an incremental improvement in pooling methods.
The paper tackles the problem of pooling layers overlooking spatial arrangement information in feature maps by proposing a novel lacunarity pooling layer that captures spatial heterogeneity at multiple scales, leading to improved feature extraction capabilities in plant image classification.
Pooling layers (e.g., max and average) may overlook important information encoded in the spatial arrangement of pixel intensity and/or feature values. We propose a novel lacunarity pooling layer that aims to capture the spatial heterogeneity of the feature maps by evaluating the variability within local windows. The layer operates at multiple scales, allowing the network to adaptively learn hierarchical features. The lacunarity pooling layer can be seamlessly integrated into any artificial neural network architecture. Experimental results demonstrate the layer's effectiveness in capturing intricate spatial patterns, leading to improved feature extraction capabilities. The proposed approach holds promise in various domains, especially in agricultural image analysis tasks. This work contributes to the evolving landscape of artificial neural network architectures by introducing a novel pooling layer that enriches the representation of spatial features. Our code is publicly available.