A comparison of global versus local color histograms for object recognition

Global color distributions have been efficiently used as signatures for object recognition. However, these methods are very sensitive to partial occlusions and to background regions. Our approach is directed to minimize these effects by working with small neighborhoods. We compare global and local color representations on an automatic object recognition system. Local representations significantly outperformed global representations in terms of recognition rates. Local color distributions are a strong constraint when objects consist of distinctive local regions. Eigenspace techniques are applied to detect discriminant local representations and support vector machines are used during the recognition process in order to maximize the recognition rate.

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