Video Hashing Based On Low-rank Frames And Tensor Decomposition

The video hashing algorithm is an efficient technique for video content management and protection that has been successfully applied to video retrieval,copy detection and authentication.It maps video into a content-based,short sequence of numbers or bits,and this sequence is called a video hash.Usually,using video hash to represent the video itself can effectively reduce the storage cost and computational complexity of the video to meet the requirements of practical applications for fast processing of video data.In general,the video hashing algorithm should meet two basic performance indicators: robustness and uniqueness.For videos,most of them will go through MPEG-4 compression,brightness adjustment and other digital operations,and the visual content is still similar to the original video despite the change of the video file data after these digital operations.Therefore,robustness requires that videos with the same or similar visual content should be mapped to the same or similar hash sequences to ensure that similar videos can be correctly identified.Uniqueness,on the other hand,requires that videos with different visual content are mapped to different hash sequences to ensure that videos with different content can be correctly distinguished.Since robustness and uniqueness have mutual constraints,designing video hashing algorithms that take into account both metrics is an important task of current research.In this paper,we study video hashing algorithms using theories and techniques such as singular value decomposition(SVD),two-dimensional discrete wavelet transform(2D-DWT)and tensor decomposition,and propose two new video hashing algorithms.The first one is a video hashing algorithm based on low-rank frames,and the second one is a video hashing algorithm based on tensor decomposition.The main research results are summarized as follows.1.Video hashing based on low-rank frames is proposed.In this section,we use singular value decomposition to construct low-rank frames,compress the low-rank frames by two-dimensional discrete wavelet transform,and generate hash with the mean value of wavelet coefficients.Singular value decomposition is a useful technique for data analysis and is widely used in signal processing,recommender systems and data compression.Two-dimensional discrete wavelet transform is a useful time-frequency analysis method that has been widely used in image processing,video processing and machine learning.In this paper,the main computational steps of the new video hashing algorithm designed by using singular value decomposition and 2D discrete wavelet transform are as follows.Firstly,the input video is pre-processed to obtain normalized video and grouped and constructed high-dimensional matrix.Then the low rank approximation representation of the high dimensional matrix is calculated using singular value decomposition and the low rank approximation representation is used to construct low rank frames.Then the low rank frames are compressed using the two-dimensional discrete wavelet transform,and the hash elements are constructed using the mean of the low frequency wavelet coefficients,and finally all the hash elements are concatenated to obtain the video hash.2.Video hashing based on tensor decomposition is designed.Tensor is a form of high-dimensional data.Tensor decomposition can obtain lowdimensional data that reflects the eigen structure information of high-dimensional data,and has been successfully applied to data mining,image compression and face recognition.In this paper,a new video hashing algorithm is designed using a tensor decomposition technique called CP decomposition,and the specific algorithm steps are as follows.The algorithm first preprocesses the input video to get normalized video and group it.Then the features are extracted from the video grouping to construct a feature tensor,followed by CP decomposition of the feature tensor.Finally,the decomposed rank one tensor is quantized to get the video hash.The performance of the algorithms in this paper is analyzed using receiver operating characteristic curves,and experiments are conducted to compare them with various video hashing algorithms in the literature.The experimental results show that the two video hashing algorithms proposed in this paper outperform the video hashing algorithms of multiple literatures in terms of robustness,uniqueness and running time,and the hash length is short enough.