Feature Combination and the kNN Framework in Object Classification
Feature Combination and the kNN Framework in Object Classification
复制标题
目标分类中的特征组合和 kNN 框架
DOI：
10.1109/tnnls.2015.2461552
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发表时间：
2016
影响因子：
10.4
通讯作者：
Naiming qi
中科院分区：
文献类型：
--
作者：
Jian Hou;Huijun Gao;Qi Xia;Naiming qi
在目标分类中，特征组合通常可以将多个互补特征的强度进行联合收割机组合，产生比任何单一特征更好的分类结果。虽然多核学习（MKL）是一种流行的方法来组合特征的对象分类，它并不总是在实际应用中表现良好。一方面，MKL中的优化过程通常涉及大量的计算和存储空间消耗。另一方面，在某些情况下，发现MKL的性能并不比基线组合方法好。这一观察促使我们研究平均组合和加权平均组合的特征组合的潜在机制。因此，我们根据经验发现，在平均组合中，最好使用最强大的特征样本而不是所有特征，而在一种类型的加权平均组合中，最好的分类精度来自于几乎稀疏的组合。我们将这些观察到的k-最近邻（kNNs）框架，在此基础上，我们进一步讨论了稀疏解和MKL的相关问题。最后，通过利用kNN框架，我们提出了一种新的加权平均组合方法，该方法在准确性和效率方面都优于MKL。我们相信，本文的工作有助于探索潜在的特征组合的机制。
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 method, which is shown to perform better than MKL in both accuracy and efficiency in experiments. We believe that the work in this paper is helpful in exploring the mechanism underlying feature combination.