In Situ Leaf Classification Using Histograms of Oriented Gradients

Abstract

Histograms of Oriented Gradients (HOGs) have proven to be a robust feature set for many visual object recognition applications. In this paper we investigate a simple but powerful approach to make use of the HOG feature set for in situ leaf classification. The contributions of this work are threefold. Firstly, we present a novel method for segmenting leaves from a textured background. Secondly, we investigate a scale and rotation invariant enhancement of the HOG feature set for texture based leaf classification - whose results compare well with a multi-feature probabilistic neural network classifier on a benchmark data set. And finally, we introduce an in situ data set containing 337 images of Lantana camara - a weed of national significance in the Australian landscape - and neighbouring flora, upon which our proposed classifier achieves high accuracy (86.07%) in reasonable time and is thus viable for real-time detection and control of Lantana camara.