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Memory-Efficient Feature Selection for Adaboost in Computer Vision

Resolve memory issues in Adaboost by storing less information, calculating values as needed, and exploring Gaussian and Gabor filters as alternative features. Progress includes work with Haar and Gaussian features. Next, will test on MMl Face DB images and videos. References to Viola-Jones and Silpachote et al.

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Memory-Efficient Feature Selection for Adaboost in Computer Vision

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  1. Adam Yeh UCF Computer Vision REU Week 4

  2. Coding Problems • Problem: Adaboost taking too much memory: • 160k features, feature value as int (4 bytes) • Maximum heap size: 1.5GB • Each picture takes 160k*4=640kB • ~2000 pictures can be trained on • Solution: store less information • Calculate values as needed, less preprocessing

  3. Feature Selection • Previously: Haar basis wavelets • Pixel sums and differences [1]

  4. Feature Selection • Gaussian Derivatives: Silapachote et al. • Calculate with varying sigma, location, and extract features

  5. Feature Selection • Gabor Filters • Harmonic (sin/cos) function multiplied by Gaussian • Use different frequencies, orientations

  6. Progress: This Week • Adaboost code w/Haar features • Still training on 1500/4500 • Started coding for Gaussian features • Obtained test images (MMI)

  7. Next Week • Test images: MMI Face DB • 800+ videos, 200+images • 8GB+ • Need to download, process, sort/organize • Other features • Coding/testing Gaussian, Gabor features • Attentional Cascade?

  8. References • [1] Viola-Jones • [2] Silpachote, P, Karuppiah, D, and Hanson, A. “Feature Selection Using Adaboost for Face Expression Recognition”. Proceedings of the Fourth IASTED International Conference: Visualization, Imaging, and Image Processing, Sept 2004. • [3] Wikipedia

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