Abstract
In this paper we propose a new general framework to obtain more distinctive local invariant features by projecting the original feature descriptors into low–dimensional feature space, while simultaneously incorporating also class information. In the resulting feature space, the features from different objects project to separate areas, while locally the metric relations between features corresponding to the same object are preserved. The low–dimensional feature embedding is obtained by a modified version of classical Multidimensional Scaling, which we call supervised Multidimensional Scaling (sMDS). Experimental results on a database containing images of several different objects with large variation in scale, viewpoint, illumination conditions and background clutter support the view that embedding class information into the feature representation is beneficial and results in more accurate object recognition.
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Raytchev, B., Kikutsugi, Y., Tamaki, T., Kaneda, K. (2011). Class-Specific Low-Dimensional Representation of Local Features for Viewpoint Invariant Object Recognition. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_20
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DOI: https://doi.org/10.1007/978-3-642-19318-7_20
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