Binary Subspace Coding for Query-by-Image Video Retrieval

TL;DR

This paper introduces a novel binary subspace coding framework for query-by-image video retrieval, addressing information loss in existing methods by preserving subspace relationships and employing efficient asymmetric hashing schemes.

Contribution

It proposes a new similarity metric, Inner-product Binary Coding, and a Bilinear Binary Coding scheme to improve retrieval accuracy and efficiency in video search using image queries.

Findings

Outperforms state-of-the-art methods on four real-world datasets.

Effectively preserves image-video relationships in a common Hamming space.

Significantly improves retrieval speed with bilinear projections.

Abstract

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.