Feature Mining for Image Classification

The efficiency and robustness of a vision system is often largely determined by the quality of the image features available to it. In data mining, one typically works with immense volumes of raw data, which demands effective algorithms to explore the data space. In analogy to data mining, the space of meaningful features for image analysis is also quite vast. Recently, the challenges associated with these problem areas have become more tractable through progress made in machine learning and concerted research effort in manual feature design by domain experts. In this paper, we propose a feature mining paradigm for image classification and examine several feature mining strategies. We also derive a principled approach for dealing with features with varying computational demands. Our goal is to alleviate the burden of manual feature design, which is a key problem in computer vision and machine learning. We include an in-depth empirical study on three typical data sets and offer theoreti...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Piotr Dollár, Zhuowen Tu, Hai Tao, Serge Belongie
• Keywords: Computer Vision, CVPR 2007, Data Mining, Feature Mining Paradigm, Feature Mining Strategies, Manual Feature Design, Various Feature Mining