Combining Active and Semi-Supervised Learning for Video Compression

Video compression algorithms manipulate video signals to dramatically reduce the storage and bandwidth required while maximizing perceived video quality. Typical video compression methods include discrete cosine transform, vector quantization, fractal compression, and discrete wavelet transform. Recently, a machine learning based approach has been proposed which converts the color images (frames) to gray scale images (frames) and the color information for only a few representative pixels is kept. A learning model is then trained to predict the color values for the gray scale pixels across frames. Selecting the most representative pixels is essentially an active learning problem, while colorization is a semi-supervised learning problem. In this paper, we propose to combine active and semi-supervised learning for video compression. The basic idea is to minimize the size of the covariance matrix of the regularized least squares estimates, in which the regression model assumes that each pixel can be reconstructed by the other pixels with similar spatial location and intensity value. The experimental results demonstrate the effectiveness of the proposed approach for video compression.     

Pointwise SAR image change detection based on stereograph model with multiple-span neighbourhood information

In this article, a novel pointwise approach is proposed for change detection in bi-temporal synthetic aperture radar (SAR) images using stereograph model. Due to the fact that SAR image suffers from the speckle noise, a pointwise approach based on a set of characteristic points only, not on the whole pixels, seems to be more efficient. Moreover, the correlations of neighbourhood points which have different locations in bi-temporal SAR images should be studied to repress the speckle in change detection. Therefore, the stereograph model, which extends the graph model to three-dimensional space, is designed to connect the local maximum pixels on bi-temporal SAR images and can be used to capture the multiple-span neighbourhood information from the edges. Furthermore, a specialized change measure function is presented to quantify the neighbourhood information from stereograph model, and thus, a novel nondense difference image (NDI) is generated. Finally, a traditional classification method is used to analyse the NDI into changed class and unchanged class. Experiments on real SAR images show that the proposed NDI can improve separability between changed and unchanged areas, and the final results possess high accuracy and strong noise immunity for change detection tasks with noise-contaminated SAR images.     

Unsupervised change detection of VHR remote sensing images based on multi-resolution Markov Random Field in wavelet domain

This paper proposes an unsupervised change detection method for very-high-resolution (VHR) remote sensing images based on multi-resolution Markov random field (MRF) model in wavelet domain. Firstly, the wavelet transform is performed on the difference image achieved by the change vector analysis (CVA) method, and the wavelet coefficients at each scale are obtained. Then, MRF model is constructed based on the wavelet coefficients. The wavelet high-frequency coefficients establish a feature field model that describes the feature attributes of each pixel location at each scale. The initial change map (changed and unchanged) at the coarse scale are generated through applying the k-means method to the wavelet low-frequency coefficients, and a label field model describing the region of the variation results is established. The label and feature field, at the same scale, got the optimized change map under the Bayesian criterion. Finally, the results of the low-resolution scale change map are directly projected as the adjacent higher-scale initial change map. The more accurate change map is obtained successively from the coarse scale to the original resolution scale, and the detection result of the original resolution is obtained at last. Experiments on Quick Bird, SPOT-5, and IKONOS optical images have demonstrated the effectiveness of the proposed method. The experimental results show that the method has better regional consistency and strong robustness.     

A hypergraph-based context-sensitive representation technique for VHR remote-sensing image change detection

This article presents a novel semi-supervised change detection approach for very-high-resolution (VHR) remote-sensing images. The proposed approach aims at extracting the change information by making full use of the context-sensitive relationships among pixels in the images. This is accomplished via a context-sensitive image representation technique based on hypergraph model. First, each temporal image is modelled as a hypergraph that utilizes a set of hyperedges to capture the context-sensitive properties of pixels in the image. Second, the difference in the bi-temporal images is measured by both the similarity and the consistency between the two hypergraphs. Finally, the changes are separated from the unchanged ones by a hypergraph-based semi-supervised classifier on the difference image. Experimental results obtained on different VHR remote-sensing data sets demonstrate the effectiveness of the proposed approach.     

基于遗传算法和形态学运算的多焦点图像融合

目前大多数图像融合算法将每个像素都独立对待,使像素之间关系割裂开来。本文提出了一种基于形态学算法和遗传算法的多焦点图像融合方法,此种方法有效地结合了像素级融合方法和特征级融合方法。其基本思想是先检测出原始图像中清晰聚焦的区域,再将这些区域提取出来,组成各部分都清晰聚焦的结果图像。实验结果证明,此方法优于Haar小波融合方法和形态学小波融合方法。特别是在原始图像没有完全配准的情况下,此种方法更为有效。     

Panchromatic sharpening of remote sensing images using a multiscale Kalman filter

This paper presents a solution to the problem of enhancing the spatial resolution of multispectral images with high-resolution panchromatic observations. The proposed method exploits the undecimated discrete wavelet transform, which is an octave bandpass representation achieved from a conventional discrete wavelet transform by omitting all decimators and up-sampling the wavelet filter bank, and the vector multiscale Kalman filter, which is used to model the injection process of wavelet details. Kalman modelization is exploited by spatial detail analysis at coarser scales in which multispectral and panchromatic representations are known. Results are presented and discussed on very-high resolution images acquired by Quickbird satellite systems. Fusion simulations on spatially degraded data and fusion tests at the full scale reveal that an accurate and reliable PAN-sharpening is achieved by the proposed method.     

一种融合小波变换与卷积神经网络的高相似度图像识别与分类算法

针对特定领域高相似度图像识别与分类问题,提出融合小波变换与卷积神经网络的高相似度图像识别与分类算法。首先,利用小波变换提取图像纹理特征,对不同类别、不同分辨率图像集进行训练并确定最佳纹理差异度参数值;其次,根据纹理差异度运用小波分解方法对图像进行子图分解,提取各子图能量特征并进行归一化处理;接着,通过卷积神经网络5层卷积和3层池化交替,将输入图像特征向量转化为一维向量;最后,通过训练次数的增加以及数据量的增大,不断优化网络参数,提高在训练集中的分类准确度,在测试集中验证权值实际准确度,得到具有最高分类准确率的卷积神经网络模型。实验选取鸡蛋、苹果两类图像数据集作为实验数据,进行鸡蛋散养或圈养识别、苹果产地判定,实验结果表明：该算法平均鉴别准确率均达90%以上。     

Bayesian change detection based on spatial sampling and Gaussian mixture model

A Gaussian mixture model (GMM) and Bayesian inferencing based unsupervised change detection algorithm is proposed to achieve change detection on the difference image computed from satellite images of the same scene acquired at different time instances. Each pixel of the difference image is represented by a feature vector constructed from the difference image values of the neighbouring pixels to consider the contextual information. The feature vectors of the difference image are modelled as a GMM. The conditional posterior probabilities of changed and unchanged pixel classes are automatically estimated by partitioning GMM into two distributions by minimizing an objective function. Bayesian inferencing is then employed to segment the difference image into changed and unchanged classes by using the conditional posterior probability of each class. Change detection results are shown on real datasets.     

Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images

In this paper, we establish a deep neural network using stacked Restricted Boltzmann Machines (RBMs) to analyze the difference images and detect changes between multitemporal synthetic aperture radar (SAR) images. Given the two multitemporal images, a difference image which shows difference degrees between corresponding pixels is generated. Then, RBMs are stacked to form a deep hierarchical neural network to learn to analyze the difference image and recognize the changed pixels and unchanged pixels. The learning process includes unsupervised layer-wise feature learning and supervised fine-tuning of network parameters. Unsupervised learning aims to learn the representation of the difference image. Supervised fine-tuning aims to learn to classify the changed and unchanged pixels. The network can learn from datasets that have few labeled data. The labeled data can be selected from the results obtained by other methods because there is no prior information in image change detection. The system learns to detect the changes instead of recognizing the changes by fixed equations as in traditional change detection algorithms. We test the network with real synthetic aperture radar datasets and the labeled samples are extracted from the results obtained, respectively, by several methods, including a thresholding method, a level set method and two clustering methods. The results achieved by the trained network outperform that of other methods.     

基于多尺度特征融合的SAR图像分割

由于存在相干斑噪声的影响,给SAR图像分割造成很大的困难,提出一种基于多尺度特征融合的SAR图像分割方法。该方法利用快速离散curvelet变换提取图像的纹理特征,利用平稳小波变换提取图像的统计特征,将两种多尺度特征融合成高维的特征向量,采用模糊C均值聚类的方法进行分割。在仿真SAR图像和真实SAR图像的分割实验结果表明,提出的方法优于单独采用小波变换进行SAR图像分割的方法,在消除均质区内碎块的同时,使得边界更为精准和平滑。     

Quantitative evaluation of a wavelet-based method in ventricular late potential detection

Ventricular late potentials (VLPs) are low-amplitude, high-frequency waveforms appearing in the terminal part of the QRS complex in electrocardiogram (ECG) of patients who are susceptible to ventricular tachycardia and sudden cardiac death, after surviving myocardial infarction. Accordingly, VLP detection presents a prominent non-invasive marker for some cardiac diseases clinically. This paper proposes a VLP detection method based on the wavelet transform and investigates its performance. In this method, a modified vector magnitude waveform is formed using discrete wavelet transform for each high-resolution ECG (HRECG) record; then, by applying the continuous wavelet transform to the QRS complex end part in this waveform, a feature vector is extracted from the resultant time-scale plot. This wavelet-based feature vector is processed by principle component analysis to reduce its dimensionality. Finally, a supervised feedforward artificial neural network, trained by a proper set of these feature vectors, is employed as a classifier. To evaluate the proposed method performance, a HRECG database consisting of the real VLP-negative and simulated VLP-positive patterns is used. In a comparative approach, different VLP detection techniques including the conventional time-domain method, developed by Simson, and some methods utilizing distinct diagnostic features are also applied to this database to investigate the capability of the proposed method in VLP analysis more completely. The results show the proposed method, employing the wavelet transform in both pre-processing and feature extraction stages, reveals high evaluation criteria (accuracy, sensitivity, and specificity) and is qualified to detect VLPs.     

Texture image retrieval using new rotated complex wavelet filters.

A new set of two-dimensional (2-D) rotated complex wavelet filters (RCWFs) are designed with complex wavelet filter coefficients, which gives texture information strongly oriented in six different directions (45 degrees apart from complex wavelet transform). The 2-D RCWFs are nonseparable and oriented, which improves characterization of oriented textures. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we propose a novel approach for texture image retrieval by using a set of dual-tree rotated complex wavelet filter (DT-RCWF) and dual-tree-complex wavelet transform (DT-CWT) jointly, which obtains texture features in 12 different directions. The information provided by DT-RCWF complements the information generated by DT-CWT. Features are obtained by computing the energy and standard deviation on each subband of the decomposed image. To check the retrieval performance, texture database D1 of 1856 textures from Brodatz album and database D2 of 640 texture images from VisTex image database is created. Experimental results indicates that the proposed method improves retrieval rate from 69.61% to 77.75% on database D1, and from 64.83% to 82.81% on database D2, in comparing with traditional discrete wavelet transform based approach. The proposed method also retains comparable levels of computational complexity.     

基于小波变换和分形维数的车牌汉字识别

提出一种基于小波变换与分形维数的车牌汉字识别方法.对字符图像进行预处理和小波变换,应用改进的微分盒维法计算图像分形盒维值,并构造特征向量,利用支持向量机分类器对字符进行分类与识别.实验结果表明,该方法对模糊字符的识别具有鲁棒性,可提高汉字识别率.     

Correcting brightness gradients in hyperspectral data from urban areas

The analysis of airborne hyperspectral data is often affected by brightness gradients that are caused by directional surface reflectance. For line scanners these gradients occur in across-track direction and depend on the sensor's view-angle. They are greatest whenever the flight path is perpendicular to the sun-target-observer plane. A common way to correct these gradients is to normalize the reflectance factors to nadir view. This is especially complicated for data from spatially and spectrally heterogeneous urban areas and requires surface type specific models. This paper presents a class-wise empirical approach that is adapted to meet the needs of such images.Within this class-wise approach, empirical models are fit to the brightness gradients of spectrally pure pixels from classes after a spectral angle mapping (SAM). Compensation factors resulting from these models are then assigned to all pixels of the image, both in a discrete manner according the SAM and in a weighted manner based on information from the SAM rule images. The latter scheme is designed in consideration of the great number of mixed pixels.The method is tested on data from the Hyperspectral Mapper (HyMap) that was acquired over Berlin, Germany. It proves superior to a common global approach based on a thorough assessment using a second HyMap image as reference. The weighted assignment of compensation factors is adequate for the correction of areas that are characterized by mixed pixels.A remainder of the original brightness gradient cannot be found in the corrected image, which can then be used for any subsequent qualitative and quantitative analyses. Thus, the proposed method enables the comparison and composition of airborne data sets with similar recording conditions and does not require additional field or laboratory measurements.     

应用Hash函数增强数字图像半易损性水印的易损性

提出了一种空间域、小波域相结合的数字图像半易损性水印算法，该算法在图像的小波变换域嵌入基于图像内容的Hash值．实验表明，该算法可以抵抗一定程度的JPEG压缩，特别是能够将轻度的JEPG压缩和对图像局部少量像素的修改区分开来．     

基于离散小波框架变换的彩色多聚焦图像融合算法

提出了一种基于离散小波框架变换的彩色多聚焦图像融合算法。首先求取各彩色多聚焦图像的灰度分量,再对各灰度分量进行离散小波框架变换,根据离散小波框架变换系数求取各图像中像素的清晰度指标,然后根据各图像中像素的清晰度指标对属于清晰区域的像素进行组合,从而得到融合后的图像。试验结果表明本文所提出的算法能够较好地解决彩色多聚焦图像融合问题。     

无边界效应D小波软阈值法去除肉品图像斑点噪声

肉品图像中的斑点噪声与肌内脂肪的颜色特征相似,要准确提取肌内脂肪,就必须先对斑点噪声进行滤除。在小波软阈值法滤除SAR图像中的斑点噪声算法基础上,结合肉品图像的特点进行了改进。首先,使用由耿则勋提出的算法,在小波分解时对左右边界进行处理,重构时外推值为0,对离散小波变换的边界效应进行处理,精确重建图像,消除小波变换的边界效应。选择了D8小波,3级分解后,在不同的阈值下进行了实验。结果表明:阈值的选取影响去噪效果,但在所有参数都相同时,改进算法消除边界效应的同时,在斑点指数和方法噪声两个客观评价指标均优于其他几种边界延拓方式,图像失真最小。     

Feature extraction using wavelet and fractal

In this paper, we are investigating the utility of several emerging techniques to extract features. A novel method of feature extraction is proposed, which includes utilizing the central projection transformation (CPT) to describe the shape, the wavelet transformation to aid in the boundary identification, and the fractal features to enhance image discrimination. It reduces the dimensionality of a two-dimensional pattern by way of a central projection approach, and thereafter, performs Daubechies' wavelet transform on the derived one-dimensional pattern to generate a set of wavelet transform sub-patterns, namely, curves that are non-self-intersecting. The divider dimensions are computed from these curves with a modified box-counting approach. These divider dimensions constitute a new feature vector for the original two-dimensional pattern, defined over the curve's fractal dimensions. We have conducted several experiments in which a set of printed Chinese characters, English letters of varying fonts and other images were classified. Based on the Euclidean distance between the different feature vectors, the experiments have satisfying results.     

基于离群点检测的图形图象噪声滤除算法

图形图象噪声过滤与修正,在媒体制作、图象分析与信息提取中起着十分重要的作用.虽然基于小波变换的算法能够对高斯噪声进行较好的滤噪处理,但对于随机分布于图象中的各种非高斯噪声仍没有普遍适用的滤噪方法.为了对这种随机分布于图象中的噪声进行有效的检测与滤除,采用对数字图象像素进行解析化描述的方法,从离群点检测的角度给出噪声的定义,并在此基础上构造了相应的图象噪声检测与滤除算法.实验结果表明,这一新方法对图象类型具有广泛的适应性和较好的噪声滤除效果,在大规模图形图象处理应用中具有实用价值.