Abstract:
Near duplicate videos (NDVs) are the primary concern for cloud storage and web search. Similar video-sharing triggers
issues related to copyright and financial loss to the maker of the video. We propose a near-duplicate video retrieval (NDVR) system.
The proposed algorithm is trained and tested on the benchmark standard dataset CC-WEB-VIDEO. For video processing, we first
split it into frames. We have processed 48 GB of frames retrieved from 80GB of video dataset. Hue Saturation Value (HSV) and
Local Binary Pattern (LBP) are used to capture the global and local features of frames. It is observed that 3% to 10% of frames have
similar frames. Therefore, the kernel-based component analysis (KPCA) algorithm is used to reduce the redundant frames. A deep
Convolution Network-based VGG16 algorithm is also used to identify the best strategy for NDV. Finally, the feature extracted from all
three techniques, HSV-KPCA, LBP-KPCA, and VGG16, are trained and tested on a radial basis function-based support vector machine
(RBF-SVM) classifier. RBF is used to address the non-linearity of nonredundant frames. Results are compared with state-of-the-art
algorithms for NDVR. The proposed system reports a higher Mean Average Precision (MAP), Area under the curve (AUC), and
accuracy than previous NDVR systems.
