A New Approach of Local Feature Descriptors using moment Invariants

Moment invariants have been widely introduced in recognizing planar objects for a few decades. This is due the robustness of moment function in distinguishing the original identity of object under various two Dimensional (2D) transformations. A set of moments computed from a planar images, represents the global description of an object’s shape and geometrical features of an image. Since global descriptor utilizes the information of a whole object or shape to describe the features of an object, it does not tolerate occlusion. If there is a mixture of regions that do not belong to the object of the interest, an additional task of segmentation is required to isolate the object for recognition. Hence, moment invariants are proposed to be employed as local descriptors for object recognition since local descriptors do not suffer from the drawbacks caused by image clutter and occlusion. A new approach of local feature descriptors using moment invariants is presented. The preliminary framework is divided into three different stages. Interest points are firstly detected in the entire image. The local descriptors are then produced by applying moment invariants on the region around the interest points. Cross-correlation is finally carried out for feature matching.