A preprint of the paper Comparing Compact Codebooks for Visual Categorization by Jan van Gemert, Cor Veenman, Arnold Smeulders, Jan-Mark Geusebroek, and myself is available online now. In the face of current large-scale video libraries, the practical applicability of content-based indexing algorithms is constrained by their efficiency. To this end, this paper compares various visual-based concept categorization techniques for efficient large-scale video indexing. In visual categorization, the popular codebook model has shown excellent categorization performance. The codebook model represents continuous visual features by discrete prototypes predefined in a vocabulary. The vocabulary size has a major impact on categorization efficiency, where a more compact vocabulary is more efficient. However, smaller vocabularies typically score lower on classification performance than larger vocabularies. This paper compares four approaches to achieve a compact codebook vocabulary while retaining categorization performance. For these four methods, we investigate the trade-off between codebook compactness and categorization performance. We evaluate the methods on more than 200 hours of challenging video data with as many as 101 semantic concepts. The results allow us to create a taxonomy of the four methods based on their efficiency and categorization performance. The paper will appear in the forthcoming special issue on Image and Video Retrieval Evaluation of Computer Vision and Image Understanding.