New 2D Feature Descriptor Free from Orientation Compensation with k-Means Clustering

In this paper, we propose two novel approaches in the field of feature description and matching. The first approach concerns the feature description and matching part, where we proposed an orientation invariant feature descriptor without an additional step dedicated to this task. We exploited the information provided by two representations of the image (intensity and gradient) for a better understanding and representation of the feature point distribution. The provided information is summarized in two cumulative histograms and used in the feature description and matching process. In the context of object detection, we introduced an unsupervised learning method based on k-means clustering. Which we used as an outlier pre-elimination phase after the matching process to improve our descriptor precision. Experiments shown its robustness to image changes and a clear increase in terms of precision of the tested descriptors after the pre-elimination phase.