Boosting Temporal Binary Coding for Large-Scale Video Search

In recent years, there has been an explosive increase in the amount of existing visual data. Hashing techniques have been successfully applied to deal with the large-scale nearest neighbor search problem among data on this massive scale. However, existing hashing methods usually learn a single hash code for each data point, and only by taking the content correlations among them into account. In practice, however, when handling complex visual data such as video, strong temporal relations exist among the successive frames. Moreover, if the preferred performance for large-scale video search is to be delivered, multiple hash codes are required for each data point in order to build multiple hash table indices. To address these problems, in this paper, we first study the multi-table learning problem for video search and attempt to learn binary codes by capturing the intrinsic video similarities from both the visual and the temporal aspects. By regarding the search over multiple tables as an ensemble prediction, the whole multi-table learning problem can be solved in a boosting learning manner to complementarily cover the nearest neighbors. For each table, a temporal binary coding solution is devised that thinks over the intrinsic relations among the visual content and the temporal consistency among the successive frames simultaneously. More specifically, we approximate the intrinsic visual similarities using a low-rank matrix based on sparse, non-negative feature expression. Furthermore, to essentially preserve the temporal consistency, we introduce a subspace rotation to model the variation among the successive frames. Under the boosting learning framework, the binary codes, hash functions and temporal variation of each table can be efficiently and jointly optimized. Extensive experiments on three large video datasets demonstrate that the proposed approach significantly outperforms a number of state-of-the-art hashing methods.