IR Feature Embedded BOF Indexing Method for Near-Duplicate Video Retrieval

Abstract

Due to the explosive increase in online videos, near-duplicate video retrieval (NDVR) has attracted much researcher attention. NDVR has very wide applications, such as copyright protection, online video monitoring, and automatic video tagging. Local features serve as elementary building blocks in many NDVR algorithms, and most of them exploit the local volume information using a bag of features (BOF) representation. However, such representation ignores potentially valuable information about the global distribution of interest points. Moreover, the discriminative power of the local descriptors is significantly reduced by the quantizer in BOF. Our motivation is that if we use the global features to classify the same or similar keyframes into the same class, it will be very useful in improving the performance of NDVR. In this paper, we present an improved radon transform (IR) feature which captures the detailed global geometrical distribution of interest points. It is calculated by using the 2D discrete Radon transform, and then applying a principal component analysis. Such IR feature is not only invariant to the geometry transformations but also robust to the noises. In addition, we propose a fusion strategy to combine the BOF representation with the global IR feature for further improving the recognition accuracy. Convincing experimental results on several publicly available datasets demonstrate that our proposed approach outperforms the state-of-the-art approaches in NDVR.