Self-Similarity and Points of Interest
In this work, we present a new approach to interest point detection. Different types of features in images are detected by using a common computational concept. The proposed approach considers the total variability of local regions. The total sum of squares computed on the intensity values of a local circular region is divided into three components: between-circumferences sum of squares, between-radii sum of squares, and the remainder. These three components normalized by the total sum of squares represent three new saliency measures, namely, radial, tangential, and residual. The saliency measures are computed for regions with different radii and scale spaces are built in this way. Local extrema in scale space of each of the saliency measures are located. They represent features with complementary image properties: blob-like features, corner-like features, and highly textured points. Results obtained on image sets of different object classes and image sets under different types of photometric and geometric transformations show high robustness of the method to intraclass variations as well as to different photometric transformations and moderate geometric transformations and compare favorably with the results obtained by the leading interest point detectors from the literature. The proposed approach gives a rich set of highly distinctive local regions that can be used for object recognition and image matching.
self-similarity the total sum of squares interest point detector visual attention visual perception linear predictors