Video copy detection method based on depth local features

Abstract

The invention discloses a video copy detection method based on depth local features, which comprises the following steps: (1) extracting frame images from video data, and then constructing an image pyramid by using different scales; (2) constructing a deep convolutional neural network model for extracting a feature map from the input image pyramid and performing feature fusion on the feature map to obtain a fused feature map; (3) training the deep convolutional neural network model in a metric learning mode; (4) extracting a fusion feature map from an image pyramid by using the trained deep convolutional neural network model; (5) extracting key points from the fusion feature map by using maximum suppression, and extracting corresponding local features according to the key points; and (6) performing video copy detection according to the local features. According to the method, the extraction speed is higher, and the local feature representation is stronger, so that accurate detection can be carried out on various complex transformed copy videos and local features, and the method has the characteristic of high robustness.

Description

technical field [0001] The invention relates to the technical field of multimedia information processing, in particular to a video copy detection method based on deep local features. Background technique [0002] In today's mobile Internet era, due to the complexity of multimedia video data, the emergence of various video editing software, and a wide range of sources, it is more difficult to prevent the indiscriminate dissemination of tampered video data. Relevant network supervision departments want to effectively supervise online multimedia video data, and cannot rely solely on human supervision and user reports. [0003] The current solution is to use traditional image processing or global feature extraction methods. Traditional algorithms have low processing efficiency and low accuracy, while global feature extraction methods are good for general editing video processing, but for various complex transformations The edited video processing effect is difficult to meet exp...

AI Technical Summary

Problems solved by technology

[0003] The current solution is to use traditional image processing or global feature extraction methods. Traditional algorithms have low processing efficiency and low accuracy, while global feature extraction methods are good for general editing video processing, but for various complex transformations The edited video processing effect is difficult to meet expectations

Both traditional image processing and global feature extraction methods have certain defects for multimedia videos on the Internet.

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