Title :
Efficient Feature Extraction for Image Classification
Author :
Zhang, Wei ; Xue, Xiangyang ; Sun, Zichen ; Guo, Yue-Fei ; Chi, Mingmin ; Lu, Hong
Author_Institution :
Fudan Univ., Shanghai
Abstract :
In many image classification applications, input feature space is often high-dimensional and dimensionality reduction is necessary to alleviate the curse of dimensionality or to reduce the cost of computation. In this paper, we extract discriminant features for image classification by learning a low-dimensional embedding from finite labeled samples. In the new feature space, intra-class compactness and extra-class separability are achieved simultaneously. Target dimensionality of the embedding is selected by spectral analysis. Our method is designed suitable for data with both uni- and multi-modal class distributions. We also develop its two-dimensional variant which makes use of the matrix representation of images. Experimental results on three real image datasets demonstrate the efficacy of our method compared to the state of the art.
Keywords :
feature extraction; image classification; image representation; image sampling; matrix algebra; cost reduction; feature extraction; finite labeled samples; image classification; image datasets; image matrix representation; multimodal class distributions; spectral analysis; two-dimensional variant; Computational efficiency; Covariance matrix; Data mining; Feature extraction; Image classification; Principal component analysis; Scattering; Spectral analysis; Testing; Training data;
Conference_Titel :
Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on
Conference_Location :
Rio de Janeiro
Print_ISBN :
978-1-4244-1630-1
Electronic_ISBN :
1550-5499
DOI :
10.1109/ICCV.2007.4409058