Abstract
In boosting-based object detectors, weak classifiers are often build on Haar-like features using conventional integral images. That approach leads to the utilization of simple rectangle-shaped structures which are only partial suitable for curved-shaped structures, as present in natural object classes such as faces. In this paper, we propose a new class of fractal features based on space-filling curves, a special type of fractals also known as Peano curves. Our method incorporates the new feature class by computing integral images along these curves. Therefore space-filling curves offer our proposed features to describe a wider variety of shapes including self-similar structures. By introducing two subtypes of fractal features, three-point and four-point features, we get a richer representation of curved and topology separated but correlated structures. We compare AdaBoost using conventional Haar-like features and our proposed fractal feature class in several experiments on the well-known MIT+CMU upright face test set and a microscopy cell test set.
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Ehlers, A., Baumann, F., Rosenhahn, B. (2014). Boosted Fractal Integral Paths for Object Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_44
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DOI: https://doi.org/10.1007/978-3-319-14364-4_44
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