A 1d convolutional network for leaf and time series classification

TL;DR

This paper introduces a 1D convolutional neural network designed for plant leaf classification and time series analysis, capable of both classification and feature extraction, with interpretability of decision signatures.

Contribution

It presents a versatile 1D CNN architecture that functions as both a classifier and a feature extractor, applicable to plant leaves and time series data, with interpretability insights.

Findings

Achieves comparable or higher accuracy than existing methods on benchmark datasets.

Produces nearly linear separable features for improved classification.

Provides human-interpretable signatures of classifier decisions.

Abstract

In this paper, a 1d convolutional neural network is designed for classification tasks of plant leaves. This network based classifier is analyzed in two directions. In the forward direction, the proposed network can be used in two ways: a classifier and an automatic feature extractor. As a classifier, it takes the simple centroid contour distance curve as the single feature and achieves comparable performance with state-of-art methods that usually require multiple extracted features. As a feature extractor, it produces nearly linear separable features, hence can be used together with other classifiers such as support vector machines to provide better performance. The proposed network adopts simple 1d input and is generally applicable for other tasks such as classifying one dimensional time series in an end-to-end fashion without changes. Experiments on some benchmark datasets show this architecture can provide classification accuracies that are comparable or higher than some existing methods. In the backward direction, methods like gradient-weighted class activation mapping and maximum activation map of neurons in the classification layer with respect to inputs are performed to help investigate and further validate that hidden signatures helping trigger the trained classifier's specific decisions can be human interpretable. Code for the paper is available at https://github.com/dykuang/Leaf_Project.