Composite description based on color vector quantization and visual primary features for CBIR tasks

Multimedia Tools and Applications - This paper presents a novel method for content-based color image retrieval that combines color vector quantization and visual primary features into a compact...     

Error sensitivity model based on spatial and temporal features

Packet loss and error propagation induced by it are significant causes of visual impairments in video applications. Most of the existing video quality assessment models are developed at frame or sequence level, which can not accurately describe the impact of packet loss on the local regions in one frame. In this paper, we propose an error sensitivity model to evaluate the impact of a single packet loss. We also make full use of the spatio-temporal correlation of the video and analyze a set of features that directly impact the perceptual quality of videos, based on the specific situation of video packet loss. With the aid of the support vector regression (SVR), these features are used to predict the error sensitivity of the local region. The proposed model is tested on six video sequences. Experimental results show that the proposed model predicts sensitivity of videos to different packet loss cases with certain reasonable accuracy, and provides good generalization ability, which turns out outperform the state-of-art image and video quality assessment methods.

     

Egocentric visual scene description based on human-object interaction and deep spatial relations among objects

Multimedia Tools and Applications - Visual Scene interpretation is one of the major areas of research in the recent past. Recognition of human object interaction is a fundamental step...     

An efficient spatial access method for spatial images containing multiple non-overlapping features

In this paper we propose and analyze a new spatial access method, namely the S*-tree, for the efficient secondary memory encoding and manipulation of images containing multiple non-overlapping features (i.e., coloured images). The S*-tree is based on a non-straightforward and space efficient extension to coloured images of its precursor, namely the S⁺-tree, which was explicitly designed for binary images. To assess experimentally the qualities of the S*-tree, we test it against the HL-quadtree, a previous spatial access method for coloured images, which is known to be space and time efficient. Our experiments show that the S*-tree reaches up to a 75% of space saving, and performs constantly less I/O accesses than the HL-quadtree in solving classical window queries.     

基于小波系数相关性的图像自适应空域隐写术

定义图像中像素的嵌入失真是图像自适应隐写中的关键。为提高图像自适应隐写的安全性,根据最小化嵌入失真原则,提出了一种基于小波系数相关性的图像自适应空域隐写术。首先以一维高通、低通滤波器为工具构造方向滤波器;然后沿水平、垂直、对角线方向对图像进行方向滤波,并根据小波系数与其邻域系数的相关性对失真函数进行设计;最后根据像素的嵌入失真值,利用网格码(STC)对秘密信息进行嵌入。实验结果表明,该隐写术能够将嵌入区域集中在内容复杂的纹理区域,且能够有效抵抗通用隐写检测算法的分析。     

Image retrieval model based on weighted visual features determined by relevance feedback

An accurate and rapid method is required to retrieve the overwhelming majority of digital images. To date, image retrieval methods include content-based retrieval and keyword-based retrieval, the former utilizing visual features such as color and brightness, and the latter utilizing keywords that describe the image. However, the effectiveness of these methods in providing the exact images the user wants has been under scrutiny. Hence, many researchers have been working on relevance feedback, a process in which responses from the user are given as feedback during the retrieval session in order to define a user’s need and provide an improved result. Methods that employ relevance feedback, however, do have drawbacks because several pieces of feedback are necessary to produce an appropriate result, and the feedback information cannot be reused. In this paper, a novel retrieval model is proposed, which annotates an image with keywords and modifies the confidence level of the keywords in response to the user’s feedback. In the proposed model, not only the images that have been given feedback, but also other images with visual features similar to the features used to distinguish the positive images are subjected to confidence modification. This allows for modification of a large number of images with relatively little feedback, ultimately leading to faster and more accurate retrieval results. An experiment was performed to verify the effectiveness of the proposed model, and the result demonstrated a rapid increase in recall and precision using the same amount of feedback.     

A SIFT features based blind watermarking for DIBR 3D images

Depth image based rendering (DIBR) is a promising technique for extending viewpoints with a monoscopic center image and its associated per-pixel depth map. With its numerous advantages including low-cost bandwidth, 2D-to-3D compatibility and adjustment of depth condition, DIBR has received much attention in the 3D research community. In the case of a DIBR-based broadcasting system, a malicious adversary can illegally distribute both a center view and synthesized virtual views as 2D and 3D content, respectively. To deal with the issue of copyright protection for DIBR 3D Images, we propose a scale invariant feature transform (SIFT) features based blind watermarking algorithm. To design the proposed method robust against synchronization attacks from DIBR operation, we exploited the parameters of the SIFT features: the location, scale and orientation. Because the DIBR operation is a type of translation transform, the proposed method uses high similarity between the SIFT parameters extracted from a synthesized virtual view and center view images. To enhance the capacity and security, we propose an orientation of keypoints based watermark pattern selection method. In addition, we use the spread spectrum technique for watermark embedding and perceptual masking taking into consideration the imperceptibility. Finally, the effectiveness of the presented method was experimentally verified by comparing with other previous schemes. The experimental results show that the proposed method is robust against synchronization attacks from DIBR operation. Furthermore, the proposed method is robust against signal distortions and typical attacks from geometric distortions such as translation and cropping.     

基于链码描述的图像图形特征提取

为提取云团反射率图像内部层次类特征和轮廓线上不规则的凹陷类特征,在构建基于图像内容的层次结构模型的基础上,提出相对极坐标系下的距离链码和基于八方向链码的累积导数和差码方法,前者用于层次类特征提取,后者用于轮廓形态特征的提取,构建梯度特征、云砧特征以及凹陷特征的提取算法.实验证明,该方法下的特征类间区分度高,运行速度快,可用于解决非刚体图像的相关特征的提取.     

Wavelet-based copyright-protection scheme for digital images based on local features

A lossless, robust copyright-protection scheme for digital images based on cryptography and watermarking is proposed. In the proposed wavelet-based scheme, the logo image is not embedded into the protected image. The secret key is generated during the embedding process using local features extracted from the perceptually prominent components of the host image after the digital wavelet transform. Digital signature and time-stamping technologies are then used for further protection. With the secret key, the logo image extraction process can be completed without the host image. Experimental results show that the proposed scheme is effective and robust against common image processing and geometric distortions.     

Soft-then-hard super-resolution mapping based on a spatial attraction model with multiscale sub-pixel shifted images

Super-resolution mapping (SRM) is a technique for exploring spatial distribution information of the land-cover classes at finer spatial resolution. The soft-then-hard super-resolution mapping (STHSRM) algorithm is a type of SRM algorithm that first estimates the soft class values for sub-pixels at the target fine spatial resolution and then predicts the hard class labels for sub-pixels. The sub-pixel shifted images from the same area can be incorporated to improve the accuracy of STHSRM algorithm. In this article, multiscale sub-pixel shifted images (MSSI) based on the fine-scale model and the coarse-scale model are utilized to increase the accuracy of STHSRM. First, class fraction images are derived from multiple sub-pixel shifted coarse spatial resolution images by soft classification. Then using the sub-pixel/sub-pixel spatial attraction model as fine-scale and the sub-pixel/pixel spatial attraction model as coarse scale, all MSSI can be derived from fraction images. The MSSI for each class are then integrated to obtain the desired fine spatial resolution images. Finally, the integrated fine spatial resolution images are used to allocate classes for sub-pixel. Experiments on two synthetic remote sensing images and a real hyperspectral remote sensing imagery show that the proposed method produces higher mapping accuracy result.     

A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.     

Data hiding in grayscale images by dynamic programming based on a human visual model

A new method for data hiding in grayscale images based on a human vision model with distortion-minimizing capabilities is proposed. Each of the eight bit planes of an input grayscale image is viewed as a binary image, into which message data are embedded horizontally. Two optimization techniques, namely, block pattern coding and dynamic programming, are proposed for image distortion minimization. Experimental results show good performs of the proposed method.     

基于定性视觉特征的裂纹缺陷的仿真

提出一种基于定性视觉特征的裂纹缺陷x光图像的仿真方法.从裂纹的定性视觉特征提取语义,进行逻辑描述,生成裂纹缺陷的骨架,对骨架各个像素分别进行运动模糊滤波,由所需裂纹的大致宽度对模糊骨架进行偏移后加权叠加.根据裂纹缺陷整体走向对整幅图进行运动模糊滤波,引入模糊几何特征,以调整仿真算法参数.实验结果表明,该方法可得到具备模糊性和随机性的各类裂纹缺陷.     

Estate price prediction system based on temporal and spatial features and lightweight deep learning model

Applied Intelligence - The development of estate price prediction systems is one of the issues that researchers are paying the most attention to. A good estate price prediction system can shorten...     

Recognition of dynamic visual images based on group transformations

A new dynamic image recognition method for image sequences based on two-level recognition of individual states and a set of states as a whole is considered in the paper. Integrated and invariant estimations of internal automorphisms are suggested. The modified collective decision-making method for dynamic image recognition uses four types of pseudodistances, in order to obtain a measure of similarity of input dynamic images with dynamic reference images depending on representation of dynamic features, as sets of numerical parameters, sets of vectors, or sets of functions. The experimental data on dynamic image recognition based on various measures of similarity of the sample with the reference pattern, which take into account isomorphic and permissible homomorphic transformations of visual projections, are presented.     

A two-level method for segmenting cytological images based on active contour model

The problem of automatic extraction of cell nuclei in cytological images for automated diagnostics of hematological diseases is considered. A combined two-level segmentation method based on an active contour model is presented. The method employs different strategies to form the active contour model at coarse and fine approximation steps. The capture range of the active contour is extended by employing a wave propagation model. Unsupervised initialization of the active contour involves binarization of the a component of the input image in the CIE Lab color space. The efficiency of the developed method is demonstrated by a computational experiment. Dmitrii Mikhailovich Murashov. Born 1958. Graduated from the Moscow Aviation Institute in 1981, majoring in automatic control systems. Received candidate’s degree (Eng.) and is an assistant professor. Currently is with Dorodnicyn Computing Centre, Russian Academy of Sciences. Scientific interests: automatic control, image processing and analysis, and pattern recognition. Author of more than 40 papers.     

Robust visual tracking based on spatial context pyramid

Multimedia Tools and Applications - In recent years, discriminative correlation filter (DCF) has gained a lot of popularity in visual tracking, mainly due to its circular sampling from limited...