Perceptual Robust Hashing for Color Images with Canonical Correlation Analysis

• URL: http://arxiv.org/abs/2012.04312v1
• Date: Tue, 8 Dec 2020 09:35:21 GMT
• Title: Perceptual Robust Hashing for Color Images with Canonical Correlation Analysis
• Authors: Xinran Li, Chuan Qin, Zhenxing Qian, Heng Yao and Xinpeng Zhang
• Abstract summary: We propose a novel perceptual image hashing scheme for color images based on ring-ribbon quadtree and color vector angle. Our scheme has satisfactory performances with respect to robustness, discrimination and security, which can be effectively used in copy detection and content authentication.
• Score: 21.22196411212803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, a novel perceptual image hashing scheme for color images is proposed based on ring-ribbon quadtree and color vector angle. First, original image is subjected to normalization and Gaussian low-pass filtering to produce a secondary image, which is divided into a series of ring-ribbons with different radii and the same number of pixels. Then, both textural and color features are extracted locally and globally. Quadtree decomposition (QD) is applied on luminance values of the ring-ribbons to extract local textural features, and the gray level co-occurrence matrix (GLCM) is used to extract global textural features. Local color features of significant corner points on outer boundaries of ring-ribbons are extracted through color vector angles (CVA), and color low-order moments (CLMs) is utilized to extract global color features. Finally, two types of feature vectors are fused via canonical correlation analysis (CCA) to prodcue the final hash after scrambling. Compared with direct concatenation, the CCA feature fusion method improves classification performance, which better reflects overall correlation between two sets of feature vectors. Receiver operating characteristic (ROC) curve shows that our scheme has satisfactory performances with respect to robustness, discrimination and security, which can be effectively used in copy detection and content authentication.

Related papers

• A Multiscale Gradient Fusion Method for Edge Detection in Color Images Utilizing the CBM3D Filter [1.54369283425087]
  A color edge detection strategy based on collaborative filtering combined with multiscale gradient fusion is proposed. The block-matching and 3D (CBM3D) filter are used to enhance the sparse representation in the transform domain. The method proposed has good noise robustness and high edge quality, which is better than the Color Sobel, Color Canny, SE and Color AGDD.
  arXiv  Detail & Related papers  (2024-08-26T04:36:10Z)
• Image contrast enhancement based on the Schrödinger operator spectrum [0.276240219662896]
  We propose a novel image contrast enhancement method based on projecting images onto the squared eigenfunctions of the two-dimensional Schr"odinger operator. This projection relies on a design parameter, $gamma$, which controls pixel intensity during image reconstruction. Results demonstrate that the proposed method effectively enhances image contrast while preserving the inherent characteristics of the original image.
  arXiv  Detail & Related papers  (2024-06-04T12:37:11Z)
• Data-Driven Bilateral Generalized Two-Dimensional Quaternion Principal Component Analysis with Application to Color Face Recognition [4.471812624045322]
  New data-driven bilateral generalized two-dimensional quaternion principal component analysis (BiG2DQPCA) is presented. BiG2DQPCA directly works on the 2D color images without vectorizing and well preserves the spatial and color information. New approach based on BiG2DQPCA is presented for color face recognition and image reconstruction with a new data-driven weighting technique.
  arXiv  Detail & Related papers  (2023-06-12T11:45:59Z)
• Detecting Recolored Image by Spatial Correlation [60.08643417333974]
  Image recoloring is an emerging editing technique that can manipulate the color values of an image to give it a new style. In this paper, we explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring. Our method achieves the state-of-the-art detection accuracy on multiple benchmark datasets and exhibits well generalization for unknown types of recoloring methods.
  arXiv  Detail & Related papers  (2022-04-23T01:54:06Z)
• Multiscale Analysis for Improving Texture Classification [62.226224120400026]
  This paper employs the Gaussian-Laplacian pyramid to treat different spatial frequency bands of a texture separately. We aggregate features extracted from gray and color texture images using bio-inspired texture descriptors, information-theoretic measures, gray-level co-occurrence matrix features, and Haralick statistical features into a single feature vector.
  arXiv  Detail & Related papers  (2022-04-21T01:32:22Z)
• Reservoir Computing Approach for Gray Images Segmentation [0.0]
  The paper proposes a novel approach for gray scale images segmentation. It is based on multiple features extraction from single feature per image pixel, namely its intensity value, using Echo state network. The newly extracted features -- reservoir equilibrium states -- reveal hidden image characteristics that improve its segmentation via a clustering algorithm.
  arXiv  Detail & Related papers  (2021-07-23T08:37:24Z)
• Generalizing Face Forgery Detection with High-frequency Features [63.33397573649408]
  Current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize. We propose to utilize the high-frequency noises for face forgery detection. The first is the multi-scale high-frequency feature extraction module that extracts high-frequency noises at multiple scales. The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective.
  arXiv  Detail & Related papers  (2021-03-23T08:19:21Z)
• Generalized Two-Dimensional Quaternion Principal Component Analysis with Weighting for Color Image Recognition [1.516937009186805]
  One of the most powerful methods of color image recognition is the two-dimensional principle component analysis (2DQPCA) approach. In this paper, a generalized 2DQPCA approach with weighting is presented with imposing $L_p$ norms on both constraint and objective functions. The numerical results based on the real face databases validate that the newly proposed method is robust to noise and performs better than the state-of-the-art 2DQPCA-based algorithms and four prominent deep learning methods.
  arXiv  Detail & Related papers  (2020-10-04T03:37:23Z)
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation [59.94819184452694]
  Depth information has proven to be a useful cue in the semantic segmentation of RGBD images for providing a geometric counterpart to the RGB representation. Most existing works simply assume that depth measurements are accurate and well-aligned with the RGB pixels and models the problem as a cross-modal feature fusion. In this paper, we propose a unified and efficient Crossmodality Guided to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively.
  arXiv  Detail & Related papers  (2020-07-17T18:35:24Z)
• Probabilistic Color Constancy [88.85103410035929]
  We define a framework for estimating the illumination of a scene by weighting the contribution of different image regions. The proposed method achieves competitive performance, compared to the state-of-the-art, on INTEL-TAU dataset.
  arXiv  Detail & Related papers  (2020-05-06T11:03:05Z)
• Residual-Sparse Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet frames [146.63177174491082]
  Fuzzy $C$-Means (FCM) algorithm incorporates a morphological reconstruction operation and a tight wavelet frame transform. We present an improved FCM algorithm by imposing an $ell_0$ regularization term on the residual between the feature set and its ideal value. Experimental results reported for synthetic, medical, and color images show that the proposed algorithm is effective and efficient, and outperforms other algorithms.
  arXiv  Detail & Related papers  (2020-02-14T10:00:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.