Compass radius estimation for improved image classification using Edge-SIFT

• It was demonstrated that compass radius in Edge-SIFT affects to classification.
• The classification performance of different radii was evaluated on eight datasets.
• It is shown that selecting a radius for each image results in better classification.
• A method to automatically estimate a better radius for each dataset is proposed.
• The estimated radius guarantees better results than the state-of-the-art.

The combination of SIFT descriptors with other features usually improves image classification, like Edge-SIFT, which extracts keypoints from an edge image obtained after applying the compass operator to a colour image. We evaluate for the first time, how the use of different radii in the compass operator affects the classification performance. We demonstrate that the value proposed in the literature, radius=4.00, is not the optimum from an image classification point of view. We also put in evidence that in ideal conditions, choosing an appropriate radius for each image yields accuracy values even higher than 95%. Finally, we propose a new method to estimate the best radius for the compass operator in each dataset. Using a training subset selected on the basis of a minimum dispersion criterion of edges density, we construct a richer dictionary for each dataset in our Bag of Words pipeline. From that dictionary it is selected a radius for the whole dataset that yields higher accuracy than using the value proposed in the literature. Using this method, we obtained improvements in the accuracy up to 24.4% in Soccer, 6.77% in COIL-RWTH-2, 4.46% in Birds, 3.82% in ImageNet_Dogs, 2.75% in ImageNet_Birds, 2.02% in Flowers and 1.75% in Caltech101 datasets.