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.

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Sandra Ober, Martin Winter, Clemens Arth, Horst Bischof
• Keywords: ICIP 2007, Image Processing, Visual Vocabulary, Visual Vocabulary Tree, Vocabulary Tree