Local Image Descriptors Using Supervised Kernel ICA
Local Image Descriptors Using Supervised Kernel ICA
复制标题
DOI：
10.1007/978-3-540-92957-4_9
复制
发表时间：
2009-01
期刊：
影响因子：
--
通讯作者：
Masaki Yamazaki;S. Fels
中科院分区：
文献类型：
--
作者：
Masaki Yamazaki;S. Fels
PCA-SIFT是SIFT的扩展，其目的是通过将PCA应用于梯度图像块来降低SIFT的高维（128维）。然而，由于其全局特征和无监督算法，PCA不是用于识别的判别表示。此外，PCA和伊卡等线性方法在非线性情况下可能会失败。在本文中，我们提出了一种新的判别方法，称为监督核伊卡（SKICA），它使用非线性核方法结合监督ICA的本地图像描述符。我们的方法融合了监督学习的优点和内核的非线性特性。使用五个不同的测试数据集，我们表明，SKICA描述符产生更好的目标识别性能比其他相关的方法具有相同的维度。基于SKICA的表示具有局部敏感性、非线性独立性和高类别可分性，为图像局部描述子提供了一种有效的方法。
PCA-SIFT is an extension to SIFT which aims to reduce SIFT's high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminative representation for recognition due to its global feature nature and unsupervised algorithm. In addition, linear methods such as PCA and ICA can fail in the case of non-linearity. In this paper, we propose a new discriminative method calledSupervisedKernel ICA (SKICA) that uses a non-linear kernel approach combined with Supervised ICA-based local image descriptors. Our approach blends the advantages of supervised learning with nonlinear properties of kernels. Using five different test data sets we show that the SKICA descriptors produce better object recognition performance than other related approaches with the same dimensionality. The SKICA-based representation has local sensitivity, non-linear independence and high class separability providing an effective method for local image descriptors.