In this work we deal with the problem of video concept detection, for the purpose of using the detection results towards more effective concept-based video retrieval. In order to handle this task, we propose using spatio-temporal video slices, called video tomographs, in the same way that visual keyframes are typically used in traditional keyframe-based video concept detection schemes. Video tomographs capture in a compact way motion patterns that are present in the video, and are used in this work for training a number of base detectors. The latter augment the set of keyframe-based base detectors that can be trained on different image representations. Combining the keyframe-based and tomograph-based detectors, improved concept detection accuracy can be achieved. The proposed approach is evaluated on a dataset that is extensive both in terms of video duration and concept variation. The experimental results manifest the merit of the proposed approach.