Video Searching and Fingerprint Detection by Using the Image Query and PlaceNet-Based Shot Boundary Detection Method

Jiang, Dayou; Kim, Jongweon

doi:10.3390/app8101735

Cited by 6 publications

(3 citation statements)

References 63 publications

(59 reference statements)

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“…In our previous research [54], we studied recognizing the scene type of a picture by using a neural network to train the images in the scene category. The place datasets we used are mainly taken from the Places database [53].…”

Section: Deep Feature Fusion-based Methods 321 Place-centric Networkmentioning

confidence: 99%

Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform

Jiang

Kim

2021

Applied Sciences

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This paper presents a new content-based image retrieval (CBIR) method based on image feature fusion. The deep features are extracted from object-centric and place-centric deep networks. The discrete cosine transform (DCT) solves the strong correlation of deep features and reduces dimensions. The shallow features are extracted from a Quantized Uniform Local Binary Pattern (ULBP), hue-saturation-value (HSV) histogram, and dual-tree complex wavelet transform (DTCWT). Singular value decomposition (SVD) is applied to reduce the dimensions of ULBP and DTCWT features. The experimental results tested on Corel datasets and the Oxford building dataset show that the proposed method based on shallow features fusion can significantly improve performance compared to using a single type of shallow feature. The proposed method based on deep features fusion can slightly improve performance compared to using a single type of deep feature. This paper also tests variable factors that affect image retrieval performance, such as using principal component analysis (PCA) instead of DCT. The DCT can be used for dimensional feature reduction without losing too much performance.

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Section: Deep Feature Fusion-based Methods 321 Place-centric Networkmentioning

confidence: 99%

Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform

Jiang

Kim

2021

Applied Sciences

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“…Discriminant means that two perceptually different videos should have significantly different hashes. Although most perceptual hashing research was performed on images [30][31][32][33], video copy detection has been investigated as well [34][35][36][37][38][39][40].…”

Section: Perceptual Hashesmentioning

confidence: 99%

Fast Fallback Watermark Detection Using Perceptual Hashes

et al. 2021

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Forensic watermarking is often used to enable the tracing of digital pirates that leak copyright-protected videos. However, existing watermarking methods have a limited robustness and may be vulnerable to targeted attacks. Our previous work proposed a fallback detection method that uses secondary watermarks rather than the primary watermarks embedded by existing methods. However, the previously proposed fallback method is slow and requires access to all watermarked videos. This paper proposes to make the fallback watermark detection method faster using perceptual hashes instead of uncompressed secondary watermark signals. These perceptual hashes can be calculated prior to detection, such that the actual detection process is sped up with a factor of approximately 26,000 to 92,000. In this way, the proposed method tackles the main criticism about practical usability of the slow fallback method. The fast detection comes at the cost of a modest decrease in robustness, although the fast fallback detection method can still outperform the existing primary watermark method. In conclusion, the proposed method enables fast and more robust detection of watermarks that were embedded by existing watermarking methods.

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“…e disadvantage is that when the light intensity changes and the lens moves rapidly, the histogram obtained will be distorted, which will lead to error detection. e pixel difference method has a low computational complexity, but it is very sensitive to pixel brightness changes and light changes caused by the motion of the camera equipment and objects in the film and television, which are likely to cause lens error detection [13,14]. In recent years, texture features [15] and scale-invariant feature conversion features [16] are also often found in literatures related to lens edge detection.…”

Section: Introductionmentioning

confidence: 99%

SIFT Feature‐Based Video Camera Boundary Detection Algorithm

Kong

2021

Complexity

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Aiming at the problem of low accuracy of edge detection of the film and television lens, a new SIFT feature-based camera detection algorithm was proposed. Firstly, multiple frames of images are read in time sequence and converted into grayscale images. The frame image is further divided into blocks, and the average gradient of each block is calculated to construct the film dynamic texture. The correlation of the dynamic texture of adjacent frames and the matching degree of SIFT features of two frames were compared, and the predetection results were obtained according to the matching results. Next, compared with the next frame of the dynamic texture and SIFT feature whose step size is lower than the human eye refresh frequency, the final result is obtained. Through experiments on multiple groups of different types of film and television data, high recall rate and accuracy rate can be obtained. The algorithm in this paper can detect the gradual change lens with the complex structure and obtain high detection accuracy and recall rate. A lens boundary detection algorithm based on fuzzy clustering is realized. The algorithm can detect sudden changes/gradual changes of the lens at the same time without setting a threshold. It can effectively reduce the factors that affect lens detection, such as flash, movies, TV, and advertisements, and can reduce the influence of camera movement on the boundaries of movies and TVs. However, due to the complexity of film and television, there are still some missing and false detections in this algorithm, which need further study.

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Video Searching and Fingerprint Detection by Using the Image Query and PlaceNet-Based Shot Boundary Detection Method

Cited by 6 publications

References 63 publications

Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform

Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform

Fast Fallback Watermark Detection Using Perceptual Hashes

SIFT Feature‐Based Video Camera Boundary Detection Algorithm

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