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Discriminative Data Transform for Image Feature Extraction and Classification
Yang Song1, Weidong Cai1, Seungil Huh2, Mei Chen3, Takeo Kanade2, Yun Zhou4, and Dagan Feng1
1BMIT Research Group, School of IT, University of Sydney, Australia
2Robotics Institute, Carnegie Mellon University, United States
3Intel Science and Technology Center on Embedded Computing, United States
4Johns Hopkins University School of Medicine, United States
Abstract. Good feature design is important to achieve effective image classification. This paper presents a novel feature design with two main contributions. First, prior to computing the feature descriptors, we propose to transform the images with learning-based filters to obtain more representative feature descriptors. Second, we propose to transform the computed descriptors with another set of learning-based filters to further improve the classification accuracy. In this way, while generic feature descriptors are used, data-adaptive information is integrated into the feature extraction process based on the optimization objective to enhance the discriminative power of feature descriptors. The feature design is applicable to different application domains, and is evaluated on both lung tissue classification in high-resolution computed tomography (HRCT) images and apoptosis detection in time-lapse phase contrast microscopy image sequences. Both experiments show promising performance improvements over the state-of-the-art.
LNCS 8150, p. 452 ff.
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© Springer-Verlag Berlin Heidelberg 2013