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 cla...
Gespeichert in:
Hauptverfasser: | , , |
---|---|
Format: | Tagungsbericht |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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. |
---|---|
ISSN: | 1522-4880 2381-8549 |
DOI: | 10.1109/ICIP.2006.313117 |