Selecting features for object detection using an AdaBoost-compatible evaluation function
Pattern Recognition Letters
Unsupervised modeling of objects and their hierarchical contextual interactions
Journal on Image and Video Processing - Special issue on patches in vision
Hierarchical learning of dominant constellations for object class recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Using multi-modal 3D contours and their relations for vision and robotics
Journal of Visual Communication and Image Representation
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With the growing interest in object categorization various methods have emerged that perform well in this challenging task, yet are inherently limited to only a moderate number of object classes. In pursuit of a more general categorization system this paper proposes a way to overcome the computational complexity encompassing the enormous number of different object categories by exploiting the statistical properties of the highly structured visual world. Our approach proposes a hierarchical acquisition of generic parts of object structure, varying from simple to more complex ones, which stem from the favorable statistics of natural images. The parts recovered in the individual layers of the hierarchy can be used in a top-down manner resulting in a robust statistical engine that could be efficiently used within many of the current categorization systems. The proposed approach has been applied to large image datasets yielding important statistical insights into the generic parts of object structure.