ICIP 2006, Atlanta, GA

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Paper Detail

Session:Image/Video Processing Applications: Object Detection and Recognition
Time:Wednesday, October 11, 09:40 - 12:20
Presentation: Poster
Topic: Other Areas and Applications: Object recognition
Authors: Keith Haynes; United States Military Academy 
 Xiuwen Liu; Florida State University 
 Washington Mio; Florida State University 
Abstract: This paper proposes a method to achieve object classification with high throughput and accuracy using a rapid classifcation tree by decoupling 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 classifcation 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.