  Efficient generation of high-quality object proposals is an essential step in
state-of-the-art object detection systems based on deep convolutional neural
networks (DCNN) features. Current object proposal algorithms are
computationally inefficient in processing high resolution images containing
small objects, which makes them the bottleneck in object detection systems. In
this paper we present effective methods to detect objects for high resolution
images. We combine two complementary strategies. The first approach is to
predict bounding boxes based on adjacent visual features. The second approach
uses high level image features to guide a two-step search process that
adaptively focuses on regions that are likely to contain small objects. We
extract features required for the two strategies by utilizing a pre-trained
DCNN model known as AlexNet. We demonstrate the effectiveness of our algorithm
by showing its performance on a high-resolution image subset of the SUN 2012
object detection dataset.
