Binary Subspace Coding for Query-by-Image Video Retrieval
This work addresses the challenge of efficient and effective video search using image queries, which is important for applications like multimedia retrieval, but it is incremental as it builds on existing hashing and subspace methods.
The paper tackles the problem of query-by-image video retrieval (QBIVR) by proposing a framework that defines a similarity-preserving distance metric and two asymmetric hashing schemes, achieving improved retrieval accuracy and efficiency on four real-world datasets compared to state-of-the-art methods.
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.