Video Query Reformulation for Near-Duplicate Detection

In this paper, we present a novel near-duplicate video detection approach based on video query reformulation to expedite the video subsequence search process. The proposed video query reformulation method addresses two key issues: 1) how to efficiently skip unnecessary subsequence matches and 2) how to effectively increase the skip probability. First, we present an incremental update mechanism that rapidly estimates the similarity between two video subsequences to skip unnecessary matches. Second, we formulate an optimization problem of subsequence partition to increase the skip probability; a trust-region-based gradient descent algorithm is applied to solve the optimization problem. Extensive experiments cover various feature representations, subsequence granularities, and baseline methods; the results demonstrate that the proposed query reformulation method is robust and efficient to deal with a variety of near-duplicates in a large-scale video dataset.