Using a Reference Point for Local Configuration of SIFT-like Features for Object Recognition with Serious Background Clutter

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Abstract

Object recognition can be performed on local or global features. While local features are more robust against occlusions, global features are more powerful to distinguish among many objects. In this paper we propose a novel approach in construction of a shape model from local features aimed at achieving high discriminative power as global features have, while keeping the robustness of local features. We utilize a common reference point expressing the relative position of local features like in a star graph representation. This model is dynamically calculated during recognition which makes it flexible. With our approach we achieve an improved recognition performance of 2% compared to other shape models and even 6% compared to approaches that do not utilize shape information.

Journal

  • IPSJ Transactions on Computer Vision and Applications

    IPSJ Transactions on Computer Vision and Applications (3), 110-121, 2011

    Information Processing Society of Japan

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