基于颜色直方图的图像检索技术

讨论了基于颜色直方图的图像检索中颜色空间和直方图距离的选择问题,提出了利用颜色直方图来检索图像的一般框架,建立了基于两种颜色空间(RGB和HSV)和四种直方图距离共八种图像检索算法,并且采用了两种客观评价标准对所建立算法的有效性进行评价。结果证明,在基于颜色直方图的应用中,HSV空间比RGB空间更有效,EMD距离在所有四种距离量度中性能较好,但计算量稍大,与EMD距离相比,虽交集距离性能稍有不足,但所需计算量较小。     

Robust image hash in Radon transform domain for authentication

Image authentication has become an emergency issue in the digital world as it can be easily tampered with the image editing techniques. In this paper, a novel robust hashing method for image authentication is proposed. The reported scheme first performs Radon transform (RT) on the image, and calculates the moment features which are invariant to translation and scaling in the projection space. Then discrete Fourier transform (DFT) is applied on the moment features to resist rotation. Finally, the magnitude of the significant DFT coefficients is normalized and quantized as the image hash bits. Experimental results show that the proposed algorithm can tolerate almost all the typical image processing manipulations, including JPEG compression, geometric distortion, blur, addition of noise, and enhancement. Compared with other approaches in the literature, the reported method is more effective for image authentication in terms of detection performance and the hash size.     

A fast and efficient image retrieval system based on color and texture features

We propose a fast and efficient image retrieval system based on color and texture features. The color features are represented by color histograms and texture features are represented by block difference of inverse probabilities (BDIP) and block variation of local correlation coefficients (BVLC). It is observed that color features in combination with the texture features derived on the brightness component provides approximately similar results when color features are combined with the texture features using all three components of color, but with much less processing time. An analysis of various distance measures reveals that the square-chord distance measure outperforms the other prominent distance measures for the proposed method. Detailed experimental analysis is carried out using precision and recall on four datasets: Corel-5K, Corel-10K, UKbench and Holidays. The time analysis is also performed to compare processing speeds of the proposed method with the existing similar best methods.     

A novel image hash algorithm resistant to print–scan

With the rapid development of multimedia technology, how to establish the integrity of images has become an unavoidable problem. The transform between a digital image and its hard-copy makes the problem more complex. To simplify the content based authentication, we propose a print–scan resistant image hashing algorithm based on the Radon and wavelet transform. The Radon transforms an image to its luminance distribution, before the wavelet extracts the relationship of the different areas from the luminance distribution. Experimental results show that our algorithm is not only robust to print–scan and other common content-preserving processing, but also discriminable to content changes.     

A blind color digital image watermarking method based on image correction and eigenvalue decomposition

With the rapid development of Internet technology, the copyright protection of color images has become more and more important. In order to fulfill this purpose, this paper designs a blind color digital image watermarking method based on image correction and eigenvalue decomposition (EVD). Firstly, all the eigenvalues of the pixel block in the color host image are obtained by EVD. Then, the sum of the absolute value of the eigenvalues is quantified by the variable quantization steps to embed the color watermark image that encrypted by affine transform and encoded by the Hamming code. If the watermarked image is processed by geometric attack, then the attacked image can be corrected by using the geometric attributes. Finally, the inverse embedding process is performed to extract the color watermark. Moreover, the advantages of the proposed method are shown as follows: (1) all Peak Signal-to-noise Ratio (PSNR) values are greater than 42 dB; (2) the average Structural Similarity Index Metric (SSIM) values are greater than 0.97; (3) the maximum embedded capacity is 0.25bpp; (4) whole running-time is less than 20 s; (5) the key space is more than 2⁴⁵⁰; (6) most Normalized Cross-correlation (NC) values are more than 0.9. Compared with the related methods, the experimental results show that the proposed method not only has better watermark invisibility and larger watermark capacity, but also has higher security and stronger robustness against geometric attacks.     

Secure and robust host-adapted color image watermarking using inter-layered wavelet-packets

This work presents a secure and robust color image watermarking for copyright protection applications, that is based on exploiting the multi-spectral properties of the primary color components of the RGB image. The proposed scheme employs the interconnection between the subbands of the primary color components in the wavelet-packet domain. The scheme is constructed to be adaptive, in the sense that the watermark bits are embedded in safe locations, depending on the inter-layer energy of coefficients in the wavelet-packets. The scheme immunity to attacks is improved by applying a two-level security procedure. To validate the high performance of the proposed scheme, several experimental tests were conducted and a comparative analysis was provided. The obtained results have shown improved watermarking robustness against a wide range of attacks while preserving a high watermarking imperceptibility.     

Dictionary-based color image retrieval using multiset theory

Dictionaries have recently attracted a great deal of interest as a new powerful representation scheme that can describe the visual content of an image. Most existing approaches nevertheless, neglect dictionary statistics. In this work, we explore the linguistic and statistical properties of dictionaries in an image retrieval task, representing the dictionary as a multiset. This is extracted by means of the LZW data compressor which encodes the visual patterns of an image. For this reason the image is first quantized and then transformed into a 1D string of characters. Based on the multiset notion we also introduce the Normalized Multiset Distance (NMD), as a new dictionary-based dissimilarity measure which enables the user to retrieve images with similar content to a given query. Experimental results demonstrate a significant improvement in retrieval performance compared to related dictionary-based techniques or to several other image indexing methods that utilize classical low-level image features.     

基于视皮层全局感知特征的顽健图像散列方法

提出了一种基于视皮层全局感知特征的感知散列方法,图像首先被低通滤波后缩放成预定尺寸,然后图像分割成依赖于密钥的重叠子图像块,每块根据人类视觉系统主视皮层的层次结构,抽取图像经视觉通道逐层处理后的方向轮廓响应,并与表面颜色信息联合形成视皮层的全局感知特征,这些特征包含了每个图像块的方向、颜色信息,使得相邻系数的关系在通常的图像处理下保持不变,利用这种不变性将所有图像块的特征信息量化、置乱后形成二值图像散列.实验表明提出的方法对JPEG压缩、图像滤波等内容保持操作具有较好的顽健性,同时具有检测恶意篡改的能力,不同图像之间具有很低的共谋概率.     

A novel blind color image watermarking using upper Hessenberg matrix

It is a challenging work to design a blind color image watermarking scheme for protecting copyright, which is different from the existing schemes used binary image or grayscale image as watermark and is also different from other non-blind watermarking schemes. In this paper, we analyze the feature of the upper Hessenberg matrix, and propose a blind color image watermarking scheme using upper Hessenberg matrix of Hessenberg transform. Arnold transform is used to improve the security, and the MD5-based Hash pseudo-random algorithm is also used to improve the robustness. In the process of watermark embedding, the encrypted watermark information is embedded into the biggest energy element of the Hessenberg matrix by quantization technique. In the process of watermark extraction, the watermark is extracted from the attacked host image with blind manner. Simulation results show that the proposed scheme outperforms other related methods in the aspects of the invisibility, robustness, capacity and computational complexity.     

Content-based image retrieval using local visual attention feature

Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, salient point based image retrieval has attracted many researchers. However, the related work is usually very time consuming, and some salient points always may not represent the most interesting subset of points for image indexing. Based on fast and performant salient point detector, and the salient point expansion, a novel content-based image retrieval using local visual attention feature is proposed in this paper. Firstly, the salient image points are extracted by using the fast and performant SURF (Speeded-Up Robust Features) detector. Then, the visually significant image points around salient points can be obtained according to the salient point expansion. Finally, the local visual attention feature of visually significant image points, including the weighted color histogram and spatial distribution entropy, are extracted, and the similarity between color images is computed by using the local visual attention feature. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal.     

基于局部Tchebichef矩的鲁棒图像水印算法

针对目前基于图像不变特征水印算法不能同时有效抵抗常规图像处理和几何攻击这一问题,提出了一种基于局部Tchebichef矩(LTMs)的图像水印新算法。首先,利用Harris-Laplace检测算子提取载体图像多尺度空间中的特征点,并通过特征选择策略获得稳定且分离的局部圆形特征区域;然后,结合主方向对齐,得到具有旋转、缩放和平移(RST)不变性的局部圆形特征区域;最后,计算局部特征区域的Tchebichef矩,采用量化调制Tchebichef低阶矩幅值将水印嵌入到局部特征区域中。实验结果表明,本文算法在获得很好的不可见性的同时,对常规图像处理、几何攻击及组合攻击具有较好的鲁棒性。     

一种新的综合颜色和纹理特征的图像检索方法

提出综合HSV颜色直方图和Gabor小波纹理特征进行检索的新方法,既利用颜色特征对图像颜色全局分布的描述,又利用纹理特征对局部空间信息的描述,避免一种特征描述图像的片面性。基于Corel库的检索实验结果表明,该方法可以取得良好的检索效果。     

基于哈尔正交函数系的抗裁剪鲁棒水印算法

提出一种基于哈尔正交函数系的抗裁剪鲁棒水印算法，该算法根据哈尔正交函数系的完备归一化正交性质，对图像进行分块哈尔正交变换，根据图像视觉系统特性和哈尔正交变换性质，提取重要的中频系数，并结合零水印嵌入技术，将水印自适应地嵌入哈尔正交变换的中频矩阵，从而提高了水印算法的有效性，对裁剪攻击具有很强的抗攻击性，实验表明：该算法具有良好的鲁棒性和有效性。     

Deep supervised hashing using quadratic spherical mutual information for efficient image retrieval

Several deep supervised hashing techniques have been proposed to allow for extracting compact and efficient neural network representations for various tasks. However, many deep supervised hashing techniques ignore several information-theoretic aspects of the process of information retrieval, often leading to sub-optimal results. In this paper, we propose an efficient deep supervised hashing algorithm that optimizes the learned compact codes using an information-theoretic measure, the Quadratic Mutual Information (QMI). The proposed method is adapted to the needs of efficient image hashing and information retrieval leading to a novel information-theoretic measure, the Quadratic Spherical Mutual Information (QSMI). Apart from demonstrating the effectiveness of the proposed method under different scenarios and outperforming existing state-of-the-art image hashing techniques, this paper provides a structured way to model the process of information retrieval and develop novel methods adapted to the needs of different applications.     

彩色夜视成像处理算法的新进展

彩色夜视成像技术是当前国内外重点发展的关键技术之一,介绍了彩色夜视成像处理算法的分类、特点及其研究进展,分析了国外一些彩色夜视系统,并重点介绍了笔者在自然感彩色夜视算法及自然感彩色夜视成像系统方面的研究进展。     

Content-based image retrieval using a fusion of global and local features

Color, texture, and shape act as important information for images in human recognition. For content-based image retrieval, many studies have combined color, texture, and shape features to improve the retrieval performance. However, there have not been many powerful methods for combining all color, texture, and shape features. This study proposes a content-based image retrieval method that uses the combined local and global features of color, texture, and shape. The color features are extracted from the color autocorrelogram; the texture features are extracted from the magnitude of a complete local binary pattern and the Gabor local correlation revealing local image characteristics; and the shape features are extracted from singular value decomposition that reflects global image characteristics. In this work, an experiment is performed to compare the proposed method with those that use our partial features and some existing techniques. The results show an average precision that is 19.60% higher than those of existing methods and 9.09% higher than those of recent ones. In conclusion, our proposed method is superior over other methods in terms of retrieval performance.     

Local quaternion PHT based robust color image watermarking algorithm

It is a challenging work to design a robust localized color image watermarking scheme against desynchronization attacks. There are two main drawbacks indwelled in current localized color image watermarking: firstly, the pure gray-based feature points detectors were utilized, in which the important color information is ignored. Secondly, the watermarking algorithms were designed mainly to mark the image luminance component only, in which the significant color channels correlation are neglected. In this paper, we propose a robust color image watermarking algorithm using local quaternion PHT (Polar Harmonic Transform), which is invariant to various noises, local geometric transformations, and color variations. Firstly, the stable color image feature points are extracted by using new color image feature point detector, in which the SIFER (Scale-Invariant Feature detector with Error Resilience) detector and color invariance model are incorporated. Then, the affine invariant local regions are built adaptively according to local image content variation. Finally, the digital watermark is embedded into the local regions by modulating the invariant quaternion PHT modulus coefficients. Experiments are carried out on a color image set collected from Internet, and the extensive experimental works have shown that the proposed color image watermarking is not only invisible and robust against common image processing operations such as median filtering, noise adding, and JPEG compression, but also has conquered those challenging desynchronization attacks.     

Saliency-directed color image segmentation using modified particle swarm optimization

Color image segmentation, an ill-posed problem, can be treated as a process of dividing a color image into some constituent regions and each region is homogeneous. In this study, a saliency-directed color image segmentation approach using “simple” modified particle swarm optimization (PSO) is proposed, in which both low-level features and high-level image semantics extracted from each color image are employed. To extract high-level image semantics from each color image, the visual attention saliency map for each color image is generated by three (color, intensity, and orientation) feature maps, which is used to guide region merging using “simple” modified PSO and a hybrid fitness function for color image segmentation. The proposed approach contains four stages, namely, color quantization, feature extraction, small region elimination, and region merging using “simple” modified PSO. Based on the experimental results obtained in this study, as compared with four comparison approaches, the proposed approach usually provides the better color image segmentation results.