This paper presents a video analysis approach based on concept detection and keyframe extraction employing a visual thesaurus representation. Color and texture descriptors are extracted from coarse regions of each frame and a visual thesaurus is constructed after clustering regions. The clusters, called region types, are used as basis for representing local material information through the construction of a model vector for each frame, which reflects the composition of the image in terms of region types. Model vector representation is used for keyframe selection either in each video shot or across an entire sequence. The selection process ensures that all region types are represented. A number of high-level concept detectors is then trained using global annotation and Latent Semantic Analysis is applied. To enhance detection performance per shot, detection is employed on the selected keyframes of each shot, and a framework is proposed for working on very large data sets.