Feature Combination and the kNN Framework in Object Classification

IEEE Transactions on Neural Networks and Learning Systems
Jian HouNaiming Qi

Abstract

In object classification, feature combination can usually be used to combine the strength of multiple complementary features and produce better classification results than any single one. While multiple kernel learning (MKL) is a popular approach to feature combination in object classification, it does not always perform well in practical applications. On one hand, the optimization process in MKL usually involves a huge consumption of computation and memory space. On the other hand, in some cases, MKL is found to perform no better than the baseline combination methods. This observation motivates us to investigate the underlying mechanism of feature combination with average combination and weighted average combination. As a result, we empirically find that in average combination, it is better to use a sample of the most powerful features instead of all, whereas in one type of weighted average combination, the best classification accuracy comes from a nearly sparse combination. We integrate these observations into the k-nearest neighbors (kNNs) framework, based on which we further discuss some issues related to sparse solution and MKL. Finally, by making use of the kNN framework, we present a new weighted average combination meth...Continue Reading

References

May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Xinbo GaoXuelong Li
Jul 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Haibo He, Yuan Cao
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Xinxing XuDong Xu

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Citations

Aug 8, 2017·IEEE Transactions on Neural Networks and Learning Systems·Murillo Guimaraes Carneiro, Liang Zhao

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