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Dual-Layer Visual Vocabulary Tree Hypotheses for Object Recognition

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Dual-Layer Visual Vocabulary Tree Hypotheses for Object Recognition
This paper introduces an efficient method to substantially increase the recognition performance of a vocabulary tree based recognition system. We propose to enhance the hypothesis obtained by a standard inverse object voting algorithm with reliable descriptor co-occurrences. The algorithm operates on different layers of a standard k-means tree benefiting from ntages of different levels of information abstraction. The visual vocabulary tree shows good results when a large number of distinctive descriptors form a large visual vocabulary. Co-occurrences perform well even on a coarse object representation with a small number of visual words. An arbitration strategy with minimal computational effort combines the specific strengths of the particular representations. We demonstrate the achieved performance boost and robustness to occlusions in a challenging object recognition task.
Sandra Ober, Martin Winter, Clemens Arth, Horst Bi
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICIP
Authors Sandra Ober, Martin Winter, Clemens Arth, Horst Bischof
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