Gradient Ordinal Signature and Fixed-Point Embedding for Efficient Near-Duplicate Video Detection

Abstract

In order to meet the requirement of large scale real-time near-duplicate video detection, this paper has achieved two goals. First, this paper proposes a more compact local image descriptor which is termed as gradient ordinal signature (GOS). GOS not only has the advantages of low dimension, simplicity in computation, and high discrimination but also is invariant to mirror reflection, rotation, and scale changes. Second, applying the characteristics of the proposed GOS and combining with the embedding theory of metric spaces, this paper proposes an efficient similarity search method based on the fixed-point embedding (FE). A main advantage of FE is that its parameters have good controllability, and its performance is stable and not sensitive to dataset changes. On the whole, the goal of our approach focuses on the speed rather than the accuracy of near-duplicate video detection. We have evaluated our method on four different settings to verify the two goals. Specifically, the tests include image and video datasets, respectively, to evaluate the performance of GOS. Experimental results demonstrate the effectiveness, efficiency, and lower memory usage of GOS. Furthermore, the third test compares FE with locality sensitivity hashing. FE also shows a speed improvement of about ten times and saves more than 60% in memory usage. The fourth test demonstrates that the combination of GOS and FE for near-duplicate video detection can achieve better overall efficiency than the state-of-the-art methods.