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首页> 外文会议>Conference on Image Extraction, Segmentation, and Recognition Oct 22-24, 2001, Wuhan, China >The Study of Sparse Features in Image Recognition
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The Study of Sparse Features in Image Recognition

机译：图像识别中的稀疏特征研究

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摘要

Feature extraction is very important to pattern recognition. For many image recognition tasks, it is very hard to directly extract the explicit geometrical features of the images. In this case, global feature extraction is often used. Principal Component Analysis (PCA) is a typical global feature extraction method. However, PCA assumes the image population as Gaussian distribution and produces a set of 'compact' features, which are the coefficients of the basis functions with largest eigenvalues. Compared with compact features of PCA, sparse features seem more attractive for recognition tasks. In this paper, three algorithms that produce sparse feature are studied. Independent Component Analysis (ICA) and sparse coding (SP) can describe non-Gaussian distribution. The discriminatory sparse coding (DSP) is a variation of SP, which incorporates class label information of the training samples. Experiments results of face recognition show sparse features have more advantage over compact features. DSP gets the best results for its clustering property of the features.
机译：特征提取对于模式识别非常重要。对于许多图像识别任务，很难直接提取图像的显式几何特征。在这种情况下，通常使用全局特征提取。主成分分析（PCA）是一种典型的全局特征提取方法。但是，PCA假定图像人口为高斯分布，并产生一组“紧凑”特征，这些特征是具有最大特征值的基函数的系数。与PCA的紧凑功能相比，稀疏功能似乎对识别任务更具吸引力。本文研究了三种产生稀疏特征的算法。独立分量分析（ICA）和稀疏编码（SP）可以描述非高斯分布。歧视性稀疏编码（DSP）是SP的一种变体，它结合了训练样本的类别标签信息。人脸识别的实验结果表明，稀疏特征比紧凑特征更具优势。 DSP因其功能的聚类特性而获得了最佳结果。

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