出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Image descriptors play an important role in any computer vision system e.g. object
recognition and tracking. Effective representation of an image is challenging due to
significant appearance changes, viewpoint shifts, lighting variations and varied object
poses. These challenges have led to the development of several features and their
representations. Spatiogram and region covariance are two excellent image descriptors
which are widely used in the field of computer vision. Spatiogram is a generalization of the
histogram and contains some moments upon the coordinates of the pixels corresponding to
each bin. Spatiogram captures richer appearance information as it computes not only
information about the range of the function like histograms, also information about the
(spatial) domain.However, there is a drawback that multi modal spatial patterns cannot be
well modelled.Region covariance descriptor provides a compact and natural way of fusing
different visual features inside a region of interest. However, it is based on a global
distribution of pixel features inside a region and loses the local structure.In this paper, we
aim toovercome the existing drawbacks of these descriptors. To this, we propose rspatiogram
and then a new hybrid descriptor is presented which is combination of rspatiogram
and traditional region covariance descriptors. The results show that our
descriptors have the discriminative capability improved in comparison with other
descriptors.
关键词：Feature Descriptor; Spatiogram; Region Covariance
