B.: Video search in concept subspace: A text-like paradigm

Though both quantity and quality of semantic concept de-tection in video are continuously improving, it still remains unclear how to exploit these detected concepts as seman-tic indices in video search, given a specific query. In this paper, we tackle this problem and propose a video search framework which operates like searching text documents. Noteworthy for its adoption of the well-founded text search principles, this framework first selects a few related con-cepts for a given query, by employing a tf-idf like scheme, called c-tf-idf, to measure the informativeness of the con-cepts to this query. These selected concepts form a concept subspace. Then search can be conducted in this concept subspace, either by a Vector Model or a Language Model. Further, two algorithms, i.e., Linear Summation and Ran