  Forthcoming surveys such as the Large Synoptic Survey Telescope (LSST) and
Euclid necessitate automatic and efficient identification methods of strong
lensing systems. We present a strong lensing identification approach that
utilizes a feature extraction method from computer vision, the Histogram of
Oriented Gradients (HOG), to capture edge patterns of arcs. We train a
supervised classifier model on the HOG of mock strong galaxy-galaxy lens images
similar to observations from the Hubble Space Telescope (HST) and LSST. We
assess model performance with the area under the curve (AUC) of a Receiver
Operating Characteristic (ROC) curve. Models trained on 10,000 lens and
non-lens containing images images exhibit an AUC of 0.975 for an HST-like
sample, 0.625 for one exposure of LSST, and 0.809 for 10-year mock LSST
observations. Performance appears to continually improve with the training set
size. Models trained on fewer images perform better in absence of the lens
galaxy light. However, with larger training data sets, information from the
lens galaxy actually improves model performance, indicating that HOG captures
much of the morphological complexity of the arc finding problem. We test our
classifier on data from the Sloan Lens ACS Survey and find that small scale
image features reduces the efficiency of our trained model. However, these
preliminary tests indicate that some parameterizations of HOG can compensate
for differences between observed mock data. One example best-case
parameterization results in an AUC of 0.6 in the F814 filter image with other
parameterization results equivalent to random performance.
