  This paper presents a novel approach for lecture video indexing using a
boosted deep convolutional neural network system. The indexing is performed by
matching high quality slide images, for which text is either known or
extracted, to lower resolution video frames with possible noise, perspective
distortion, and occlusions. We propose a deep neural network integrated with a
boosting framework composed of two sub-networks targeting feature extraction
and similarity determination to perform the matching. The trained network is
given as input a pair of slide image and a candidate video frame image and
produces the similarity between them. A boosting framework is integrated into
our proposed network during the training process. Experimental results show
that the proposed approach is much more capable of handling occlusion, spatial
transformations, and other types of noises when compared with known approaches.
