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|Title:||Learning cascaded reduced - set SVMs using linear programming|
|Citation:||Digital Image Computing : Techniques and Applications (DICTA'08), 1-3 December, 2008; pp. 619-62|
|Conference Name:||International Conference on Digital Image Computing - Techniques and Applications, (2008 : Canberra, ACT, Australia)|
|Junae Kim, Chunhua Shen and Lei Wang|
|Abstract:||This paper proposes a simple and efficient detection framework that uses reduced-set kernels. We first describe our approach which reduces the number of kernels. A convex optimization method is used for calculating the reduced sets. Following this, we propose a method that optimally designs the cascade. Our experimental results indicate that our method minimizes complexity regarding the number of kernels in the cascaded structure while preserving the low error rates. Our algorithm generates the optimal weight of kernels for each cascade stage. This proposed algorithm achieves high detection-rates at low computational cost.|
|Rights:||Copyright © 2008 by The Institute of Electrical and Electronics Engineers, Inc. – All Rights Reserved|
|Appears in Collections:||Computer Science publications|
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