  The query-by-image video retrieval (QBIVR) task has been attracting
considerable research attention recently. However, most existing methods
represent a video by either aggregating or projecting all its frames into a
single datum point, which may easily cause severe information loss. In this
paper, we propose an efficient QBIVR framework to enable an effective and
efficient video search with image query. We first define a
similarity-preserving distance metric between an image and its orthogonal
projection in the subspace of the video, which can be equivalently transformed
to a Maximum Inner Product Search (MIPS) problem.
  Besides, to boost the efficiency of solving the MIPS problem, we propose two
asymmetric hashing schemes, which bridge the domain gap of images and videos.
The first approach, termed Inner-product Binary Coding (IBC), preserves the
inner relationships of images and videos in a common Hamming space. To further
improve the retrieval efficiency, we devise a Bilinear Binary Coding (BBC)
approach, which employs compact bilinear projections instead of a single large
projection matrix. Extensive experiments have been conducted on four real-world
video datasets to verify the effectiveness of our proposed approaches as
compared to the state-of-the-arts.
