The Optimal Distance Measure for Object Detection

We develop a multi-class object detection framework whose core component is a nearest neighbor search over object part classes. The performance of the overall system is critically dependent on the distance measure used in the nearest neighbor search. A distance measure that minimizes the mis-classification risk for the 1-nearest neighbor search can be shown to be the probability that a pair of input image measurements belong to different classes. In practice, we model the optimal distance measure using a linear logistic model that combines the discriminative powers of more elementary distance measures associated with a collection of simple to construct feature spaces like color, texture and local shape properties. Furthermore, in order to perform search over large training sets efficiently, the same framework was extended to find hamming distance measures associated with simple discriminators. By combining this discrete distance model with the continuous model, we obtain a hierarchica...

• Type: Conference
• Year: 2003
• Where: CVPR
• Authors: Shyjan Mahamud, Martial Hebert
• Keywords: Computer Vision, CVPR 2003, Discrete Distance Model, Elementary Distance Measures, Hierarchical Distance Model, Nearest Neighbor Search, Optimal Distance Measure