This paper presents an approach on high-level feature detection within video documents, using a Region Thesaurus and Latent Semantic Analysis. A video shot is represented by a single keyframe. MPEG-7 features are extracted from coarse regions of it. A clustering algorithm is applied on all extracted regions and a region thesaurus is constructed. Its use is to assist to the mapping of low- to high-level features by a model vector representation. Latent Semantic Analysis is then applied on the model vectors to exploit the latent relations among regions types aiming to improve detection performance. The proposed approach is thoroughly examined using TRECVID 2007 development data.