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Large video collections such as YouTube contain many different video genres, while in many applications the user might be interested in one or two specific video genres only. Thus, when users are querying the system with a specific semantic concept like AnchorPerson, and MovieStars, they are likely aiming a genre specific instantiation of this concept. Existing methods treat this problem as a classical learning problem leading to unnecessarily complex models. We propose a framework to detect visual-based genre-specific concepts in a more efficient and accurate way. We do so by using a two-step framework distinguishing two different levels. Genre-specific concept models are trained based on a training set with data labeled at video level for genres and at shot level for semantic concepts. In the classification stage, video genre classification is applied first to reduce the entire data set to a relatively small subset. Then, the genre-specific concept models are applied to this subset only. Experiments have been conducted on a small 28-h data set for genre-specific concept detection and a 4168-h (80 thinspace031 videos) benchmark data set for genre-specific topic search. Experimental results show that our proposed two-step method is more efficient and effective than existing methods which do not consider the different semantic levels between video genres and semantic concepts for both the indexing and the search tasks. When filtering out 80% of the data set, the average performance loss is about 11.3% for genre-specific concept detection and 31.5% for genre-specific topic search, while the processing speed increases hundreds of times for different video genres.