Title :
In Situ Leaf Classification Using Histograms of Oriented Gradients
Author :
Alex Olsen;Sunghyu Han;Brendan Calvert;Peter Ridd;Owen Kenny
Author_Institution :
Coll. of Sci., Technol. &
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.
Keywords :
"Feature extraction","Image segmentation","Image color analysis","Histograms","Shape","Neural networks","Real-time systems"
Conference_Titel :
Digital Image Computing: Techniques and Applications (DICTA), 2015 International Conference on
DOI :
10.1109/DICTA.2015.7371274