Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis

TL;DR

This paper introduces a lacunarity pooling layer that captures spatial heterogeneity in feature maps, enhancing neural network performance in plant image classification by analyzing texture patterns at multiple scales.

Contribution

The novel lacunarity pooling layer is designed to improve spatial feature extraction and can be integrated into existing neural networks for better texture analysis.

Findings

Improved accuracy in plant image classification tasks.

Enhanced ability to capture intricate spatial patterns.

Layer's effectiveness demonstrated through experiments.

Abstract

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.