In this paper, we present an integrated system for news video retrieval. The proposed system incorporates both speech and visual information in the search mechanisms. The initial search is based on the automatic speech recognition (ASR) transcript of video. Based on the relevant shots selected from the initial search round, keyword histograms are automatically generated for the refinement of the search query, such that the reformulated query fits better to the target topic. We have also developed an image-based refinement module, which uses the region analysis of the video key-frames. SR-tree like indexing structure is constructed for the region features, and the image-to-image similarity is computed using the Earth Mover's Distance. By performing a series of relevance feedback processes, the set of the true relevant shots is expanded significantly. The proposed system has been applied to a large open-benchmark news video dataset, and very satisfactory improvements have been obtained by applying the proposed automatic query expansion and the region-based refinement.