Robust image hashing using SIFT feature points and DWT approximation coefficients

Vadlamudi, Lokanadham Naidu; Vaddella, Rama Prasad V.; Vasumathi, D.

doi:10.1016/j.icte.2017.12.004

Cited by 12 publications

(13 citation statements)

References 14 publications

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“…Qin et al [4] performed DCT transformation on the image blocks containing rich edge information, and the coefficient features and position information were processed by principal component analysis (PCA) dimensionality reduction to generate hash. Vadlamudi et al [5] performed two-dimensional DWT decomposition on overlapping blocks containing feature points, and used the row average of DWT approximation coefficients to generate hash sequence. In scheme [6], Tang et al applied discrete Fourier transform (DFT) to each row of the image processed by log-polar transformation (LPT), and used the amplitude of the DFT coefficient to construct a rotationresistant feature matrix, and finally generated hash sequence by multidimensional scale decomposition (MDS).…”

Section: A Based On Invariant Feature Transformationmentioning

confidence: 99%

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Perceptual Image Hashing Based on Three-Dimensional Global Features and Image Energy

Yuan

Zhao

2021

IEEE Access

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In order to improve classification performance and operating efficiency of the hash algorithm, this paper proposes a novel hash algorithm that combines three-dimensional global features and local energy features. During the stage of three-dimensional features extraction, the image is firstly compressed by SVD decomposition to form a secondary image. Then the statistical features of the secondary image at the threedimensional visual angle are extracted as the global features. Finally, the global feature hash is generated by using the relationship between the statistical features of the image layers from different three-dimensional visual angles. In the energy feature extraction stage, the luminance image is divided into blocks, and then the energy value of each image sub-block is obtained. The multi-directional energy change features are taken as the local features of the image. Subsequent experimental results prove the effectiveness of the proposed algorithm. The algorithm not only has good robustness to the conventional content-preserving operations, but also achieves a good balance between discrimination capability and robustness. In addition, compared with several state-of-the-art schemes, this algorithm has the best ROC curve, the shortest running time and the best local tamper detection ability.

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Section: A Based On Invariant Feature Transformationmentioning

confidence: 99%

“…i,j are arranged and combined according to (4) to form a secondary image block p i,j , with size of (n/2)× 8. All the secondary image blocks are rearranged according to (5) to form the secondary image P with size of (M/2) × (8M/n).…”

Section: B Global Features Extraction Of 3d Perspectivementioning

confidence: 99%

Perceptual Image Hashing Based on Three-Dimensional Global Features and Image Energy

Yuan

Zhao

2021

IEEE Access

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“…Existing hash algorithms extract image features by image transformation or combining multiple transformations, such as discrete cosine transform (DCT) [1]- [3], discrete wavelet transform (DWT) [4], [5], discrete Fourier transform (DFT) [6]- [9], log-polar transform (LT) [10]. Ou et al [1] performed one-dimensional DCT transformation on the coefficients after the Radon transform, and extracted the statistical characteristics of the low frequency AC coefficient components as the image hash.…”

Section: A Based On Image Transformationmentioning

confidence: 99%

“…Tang et al [3] also proposed the hashing scheme combining LLE with color vector angle and DCT. Vadlamudi et al [4] proposed a method of combining feature points with DWT transform coefficients to design image hash sequences. Tang et al [5] constructed image hash by Gabor filtering and DWT transform of images, which not only has good security performance, but also achieves a good balance between robustness and discrimination.…”

Section: A Based On Image Transformationmentioning

confidence: 99%

Perceptual Image Hashing Based on Color Structure and Intensity Gradient

Zhao

Yuan

2020

IEEE Access

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In order to improve classification performance of the hash algorithm for color images, this paper presents an hash algorithm that based on three-dimensional color structure features and luminance gradient feature. Firstly, the algorithm preprocesses the input image to produce a secondary image, and extracts the color opponent component from the secondary image. Secondly, three-dimensional external structural features are constructed by using the peak curve, mean curve, and valley curve of the color opponent component. Thirdly, the overlapping points of the peak points in different visual angles and the valley points in different visual angles are selected as the salient points. The position information of the salient points are used to construct internal features. The gradient feature is constructed by using pixel changes in four directions of Y component of luminance image in YCbCr color space. Finally, the three-dimensional color structure features and luminance gradient feature are combined and disturbed to obtain the final hash sequence. Experiment results show that the proposed scheme has preferable robustness and discrimination. Compared with some existing schemes, the proposed scheme has better classification performance, shorter hash length and less average time of hash generation, and it has good detection performance in image copy detection and image tampered detection. INDEX TERMS Image hashing, copy and tampered detection, luminance gradient, three-dimensional color features.

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“…According to the principle of stereo vision [30,31], it is necessary to match the corresponding points from two images in order to obtain the 3D thickness information from 2D images [32]. But due to the fact that the shape of rice is similar to each other and the surface texture is scarce, it is impossible to realize matching directly using the traditional matching algorithm [33], which greatly increases the difficulty of extracting rice thickness from images.…”

Section: Introductionmentioning

confidence: 99%

Novel and Automatic Rice Thickness Extraction Based on Photogrammetry Using Rice Edge Features

Kong

Fang

et al. 2019

Sensors

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The dimensions of phenotyping parameters such as the thickness of rice play an important role in rice quality assessment and phenotyping research. The objective of this study was to propose an automatic method for extracting rice thickness. This method was based on the principle of binocular stereovision but avoiding the problem that it was difficult to directly match the corresponding points for 3D reconstruction due to the lack of texture of rice. Firstly, the shape features of edge, instead of texture, was used to match the corresponding points of the rice edge. Secondly, the height of the rice edge was obtained by way of space intersection. Finally, the thickness of rice was extracted based on the assumption that the average height of the edges of multiple rice is half of the thickness of rice. According to the results of the experiments on six kinds of rice or grain, errors of thickness extraction were no more than the upper limit of 0.1 mm specified in the national industry standard. The results proved that edge features could be used to extract rice thickness and validated the effectiveness of the thickness extraction algorithm we proposed, which provided technical support for the extraction of phenotyping parameters for crop researchers.

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Robust image hashing using SIFT feature points and DWT approximation coefficients

Cited by 12 publications

References 14 publications

Perceptual Image Hashing Based on Three-Dimensional Global Features and Image Energy

Perceptual Image Hashing Based on Three-Dimensional Global Features and Image Energy

Perceptual Image Hashing Based on Color Structure and Intensity Gradient

Novel and Automatic Rice Thickness Extraction Based on Photogrammetry Using Rice Edge Features

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