Shearlet Based Video Fingerprint for Content-Based Copy Detection

Po, Lam-Man; Li, Mengyang; Xu, Xuyuan; Jian, Wei-hua; Wong, Kwok-Wo; Cheung, Keith W.

doi:10.4236/jsip.2016.72010

Cited by 10 publications

(4 citation statements)

References 23 publications

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“…When the intensity in the red channel is denoted by red, the intensity in the green channel by Green, and the intensity in the blue channel by blue. Recognize that the particular code to implement such an algorithm seems like Yuan et al (2016).…”

Section: Image Preprocessingmentioning

confidence: 99%

Discrete Shearlet Transform and Lempel-Ziv Welch Coding for Lossless Fingerprint Image Compression

Kadim,

Guirguis,

Elsayed

2024

Journal of Computer Science

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Image compression is a crucial task in image processing and in the process of sending and receiving files. There is a need for effective techniques for image compression as the raw images require large amounts of disk space to defect during transportation and storage operations. The most important objective of image compression is to decrease the redundancy of the image which helps in increasing the storage capacity and then efficient transmission. This study introduces a system for lossless image compression that is built to work on fingerprint image compression. It uses lossless compression to take care of the first image during processing. However, there is a serious problem which is the low ratio of compression. In order to make the ratio higher, there are five lossless compression techniques used in this study which are Elias Gamma Coding (EGC), Huffman Coding (HC), Arithmetic Coding (AC), Run-Length Encoding (RLE) and Lempel Ziv Welch (LZW). With these techniques, there are three types of transforms are used; they are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Discrete Shearlet Transform (DST). The results conclude that discrete shearlet transform with the Lempel-Ziv Welch coding technique outperforms the other lossless compression techniques and its Compression Ratio (CR) is 3.678023.

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Section: Image Preprocessingmentioning

confidence: 99%

Discrete Shearlet Transform and Lempel-Ziv Welch Coding for Lossless Fingerprint Image Compression

Kadim,

Guirguis,

Elsayed

2024

Journal of Computer Science

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“…The use of a Discrete Wavelet Transform (DWT) to generate a global fingerprint is proposed in [23]. That fingerprint is robust against luminance change, salt and pepper noise, Gaussian noise, text insertion, letter box, rotation, frame dropping, and time shifting.…”

Section: Related Workmentioning

confidence: 99%

“…Some methods are robust only against some specific attacks, for example, flip and rotation [30, 31]. Nevertheless, just a few methods are robust against camera recording or temporal domain changes [23, 29, 32], which are very common and severe attacks [10]. …”

Section: Related Workmentioning

confidence: 99%

Towards a Video Passive Content Fingerprinting Method for Partial-Copy Detection Robust against Non-Simulated Attacks

Guzman-Zavaleta

Feregrino-Uribe

2016

PLoS ONE

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Passive content fingerprinting is widely used for video content identification and monitoring. However, many challenges remain unsolved especially for partial-copies detection. The main challenge is to find the right balance between the computational cost of fingerprint extraction and fingerprint dimension, without compromising detection performance against various attacks (robustness). Fast video detection performance is desirable in several modern applications, for instance, in those where video detection involves the use of large video databases or in applications requiring real-time video detection of partial copies, a process whose difficulty increases when videos suffer severe transformations. In this context, conventional fingerprinting methods are not fully suitable to cope with the attacks and transformations mentioned before, either because the robustness of these methods is not enough or because their execution time is very high, where the time bottleneck is commonly found in the fingerprint extraction and matching operations. Motivated by these issues, in this work we propose a content fingerprinting method based on the extraction of a set of independent binary global and local fingerprints. Although these features are robust against common video transformations, their combination is more discriminant against severe video transformations such as signal processing attacks, geometric transformations and temporal and spatial desynchronization. Additionally, we use an efficient multilevel filtering system accelerating the processes of fingerprint extraction and matching. This multilevel filtering system helps to rapidly identify potential similar video copies upon which the fingerprint process is carried out only, thus saving computational time. We tested with datasets of real copied videos, and the results show how our method outperforms state-of-the-art methods regarding detection scores. Furthermore, the granularity of our method makes it suitable for partial-copy detection; that is, by processing only short segments of 1 second length.

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“…Video features include color, gradient, brightness, edge and shape structure [10]. LBP is mainly used for video texture extraction and statistical histogram of LBP feature spectrum is used as video feature vector.…”

Section: Video Feature Extractionmentioning

confidence: 99%

Video Copy Detection based on Uniform Local Binary Pattern

Hou¹,

Wang²,

Liu³

2018

dtcse

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Video copy detection techniques are used to detect copies of video widely, this paper proposed a new algorithm based on spatiotemporal analysis for copy detection and compares detection precision and efficiency with existing algorithms. The descriptor encodes the structure of video key frames by computing uniform Local Binary Pattern with rotation invariance. Besides, Chi-square tests are employed to speed up the matching process. The proposed algorithm can deal with various kinds of video transformations, such as brightness conversion, sharpen, contrast and gray scale, especially for video rotation which is not well addressed in existing algorithms. The results of experiments tested on TRECVID 2015 dataset, experimental results indicate that precision and recall are improved, proposed algorithm is with good robustness and discrimination accuracy, detection performance is improved further.

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Shearlet Based Video Fingerprint for Content-Based Copy Detection

Cited by 10 publications

References 23 publications

Discrete Shearlet Transform and Lempel-Ziv Welch Coding for Lossless Fingerprint Image Compression

Discrete Shearlet Transform and Lempel-Ziv Welch Coding for Lossless Fingerprint Image Compression

Towards a Video Passive Content Fingerprinting Method for Partial-Copy Detection Robust against Non-Simulated Attacks

Video Copy Detection based on Uniform Local Binary Pattern

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