Group-sensitive multiple kernel learning for object categorization
Group-sensitive multiple kernel learning for object categorization
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DOI：
10.1109/iccv.2009.5459172
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发表时间：
2009-09
期刊：
影响因子：
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通讯作者：
Jingjing Yang;Yuanning Li;Yonghong Tian;Ling-yu Duan;Wen Gao
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文献类型：
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作者：
Jingjing Yang;Yuanning Li;Yonghong Tian;Ling-yu Duan;Wen Gao
在本文中，我们提出了一个组敏感的多核学习（GS-MKL）方法，以适应对象分类的类内多样性和类间相关性。通过在图像和对象类别之间引入一个中间表示“组”，GS-MKL试图为每个组找到合适的核组合，以获得对象类别的更精细描述。对于每个类别，组内的图像共享一组核权重，而来自不同组的图像可以采用不同的核权重集合。在GS-MKL中，这种组敏感的内核组合与基于多内核的分类器一起以联合方式进行优化，以寻求捕获多样性和保持每个类别的不变性之间的权衡。大量的实验表明，我们提出的GS-MKL方法在三个具有挑战性的数据集上取得了令人鼓舞的性能。
In this paper, we propose a group-sensitive multiple kernel learning (GS-MKL) method to accommodate the intra-class diversity and the inter-class correlation for object categorization. By introducing an intermediate representation “group” between images and object categories, GS-MKL attempts to find appropriate kernel combination for each group to get a finer depiction of object categories. For each category, images within a group share a set of kernel weights while images from different groups may employ distinct sets of kernel weights. In GS-MKL, such group-sensitive kernel combinations together with the multi-kernels based classifier are optimized in a joint manner to seek a trade-off between capturing the diversity and keeping the invariance for each category. Extensive experiments show that our proposed GS-MKL method has achieved encouraging performance over three challenging datasets.