Comparative study of global invariant descriptors for object recognition

Although many object invariant descriptors have been proposed in the literature, putting them into practice to obtain a robust recognition system that is able to face several perturbations is still a studied problem. After presenting the most commonly used global invariant descriptors, a comparative study permits us to show their ability to discriminate between objects with little training. The Columbia Object Image Library database (COIL-100), which presents a same object translated, rotated, and scaled, is used to test the invariant features of geometrical transforms. Partial object occultation or presence of complex background are examples of used images to test the robustness of the studied descriptors. We compare them in both a global and a local context (computed on the neighborhood of a pixel). The scale invariant feature transform descriptor is used as a reference for local invariant descriptors. This study shows the relative performance of invariant descriptors used in both a global and a local context and identifies the different situations for which they are best suited.

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