Recognition using Rapid Classification Tree

This paper proposes a method to achieve object classification with high throughput and accuracy using a rapid classification tree. To achieve this, we decouple the training and test stages. During the training stage, we learn optimal discriminatory features from the training set and then train a classifier with high accuracy. Then we create a classification tree, where each node uses a lookup table to store the solutions, resulting high throughput at the test stage. To make the lookup tables feasible for applications, we learn a projection matrix through stochastic optimization. We illustrate the effectiveness of the proposed method using several datasets; our results show the proposed method achieves often several orders of magnitudes of improvement in throughput while maintaining a similar accuracy.