  Object proposal algorithms have shown great promise as a first step for
object recognition and detection. Good object proposal generation algorithms
require high object recall rate as well as low computational cost, because
generating object proposals is usually utilized as a preprocessing step. The
problem of how to accelerate the object proposal generation and evaluation
process without decreasing recall is thus of great interest. In this paper, we
propose a new object proposal generation method using two-stage cascade SVMs,
where in the first stage linear filters are learned for predefined quantized
scales/aspect-ratios independently, and in the second stage a global linear
classifier is learned across all the quantized scales/aspect-ratios for
calibration, so that all the proposals can be compared properly. The proposals
with highest scores are our final output. Specifically, we explain our
scale/aspect-ratio quantization scheme, and investigate the effects of
combinations of $\ell_1$ and $\ell_2$ regularizers in cascade SVMs with/without
ranking constraints in learning. Comprehensive experiments on VOC2007 dataset
are conducted, and our results achieve the state-of-the-art performance with
high object recall rate and high computational efficiency. Besides, our method
has been demonstrated to be suitable for not only class-specific but also
generic object proposal generation.
