Non-negative locality-constrained vocabulary tree for finger vein image retrieval

Finger vein image retrieval is a biometric identification technology that has recently attracted a lot of attention. It has the potential to reduce the search space and has attracted a considerable amount of research effort recently. It is a challenging problem owing to the large number of images in biometric databases and the lack of efficient retrieval schemes. We apply a hierarchical vocabulary tree modelbased image retrieval approach because of its good scalability and high efficiency.However, there is a large accumulative quantization error in the vocabulary tree (VT)model thatmay degrade the retrieval precision. To solve this problem, we improve the vector quantization coding in the VT model by introducing a non-negative locality-constrained constraint: the non-negative locality-constrained vocabulary tree-based image retrieval model. The proposed method can effectively improve coding performance and the discriminative power of local features. Extensive experiments on a large fused finger vein database demonstrate the superiority of our encoding method. Experimental results also show that our retrieval strategy achieves better performance than other state-of-theart methods, while maintaining low time complexity.     

Edge based features for content based image retrieval

The common problem in content based image retrieval (CBIR) is selection of features. Image characterization with lesser number of features involving lower computational cost is always desirable. Edge is a strong feature for characterizing an image. This paper presents a robust technique for extracting edge map of an image which is followed by computation of global feature (like fuzzy compactness) using gray level as well as shape information of the edge map. Unlike other existing techniques it does not require pre segmentation for the computation of features. This algorithm is also computationally attractive as it computes different features with limited number of selected pixels.     

基于第二代Curvelet变换的低对比度图像增强

针对传统图像增强方法用于低对比度图像时,存在对噪声敏感、局部过增强等问题,提出了一种基于第二代 Curvelet 变换的增强方法。将图像进行多尺度多方向的 Curvelet 变换;引入分段非线性函数的思想,调整低频子带系数,提高图像整体的对比度;对各尺度的高频子带系数进行非线性加权,增强图像细节,并进行阈值降噪。实验表明,该方法优于常用的空间域直方图均衡化和小波域图像增强法,能有效地提高图像的对比度、降低噪声,并且较好地保留边缘信息,具有良好的视觉效果。     

基于第二代Curvelet变换的自适应图像增强

Curvelet是继小波和Ridgelet之后一种新的图像多尺度表示方法,Curvelet具有多尺度,多方向的特性,属于高度各向异性的变换。第二代Curvelet变换克服了第一代Curvelet变换的高数据冗余度问题,特别是基于”Wrapping”方式的第二代离散Curvelet算法,不仅运算快速、几何真实,而且快速可逆。因此,将第二代Curvelet变换用于图像增强,并通过自适应地确定Curvelet分解子带的噪声水平,实现了一种自适应图像增强方法。实验结果表明,同基于小波变换的图像增强方法相比,该方法具有明显的优势。     

ICTEDCT-CBIR: Integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval

A novel Integrated Curvelet-based image retrieval scheme (ICTEDCT-CBIR) has been proposed, for the purpose of effectively retrieving more similar images from large digital image databases. The proposed model Integrates Curvelet Multiscale ridgelets with Region-based vector codebook Subband Clustering for enhanced dominant colors extraction and texture analysis. An important ingredient of the curvelet transform is to restore sparsity by reducing redundancy across scales. The discrete curvelet transform makes use of a dyadic sequence of scales, and a bank of filters with the property that the pass band filter is concentrated near the frequencies. An enhanced Region-based vector codebook Sub band Clustering (RBSC) has been proposed for effectively extract dominant colors from the color histogram of the transformed image sub-bands. An integrated matching scheme, based on most similar Highest Priority (MSHP) principle, is used to compare the query and target images. Experimental analysis has been carried out to verify the efficiency of the proposed ICTEDCT-CBIR model. Experimental results showed that the proposed approach has better retrieval performance. First, curvelets capture more accurate texture information. Second, as curvelets are tuned to different orientations, it captured more accurate directional features than wavelets. As the experimental results indicated, the proposed technique outperforms other retrieval schemes in terms of average precision with higher precision-recall crossover point values.     

基于加权特征空间信息视觉词典的图像检索模型

针对传统的视觉词袋模型中视觉词典对底层特征量化时容易引入量化误差,以及视觉单词的适用性不足等问题,提出了基于加权特征空间信息视觉词典的图像检索模型。从产生视觉词典的常用聚类算法入手,分析和探讨了聚类算法的特点,考虑聚类过程中特征空间的特征分布统计信息,通过实验对不同的加权方式进行对比,得出效果较好的均值加权方案,据此对视觉单词的重要程度加权,提高视觉词典的描述能力。对比实验表明,在ImageNet图像数据集上,相对于同源视觉词典,非同源视觉词典对视觉空间的划分影响较小,且基于加权特征空间信息视觉词典在大数据集上更加有效。     

Passive steganalysis based on higher order image statistics of curvelet transform

Steganographic techniques accomplish covert communication by embedding secret messages into innocuous digital images in ways that are imperceptible to the human eye. This paper presents a novel passive steganalysis strategy in which the task is approached as a pattern classification problem. A critical part of the steganalyser design depends on the selection of informative features. This paper is aimed at proposing a novel attack with improved performance indices with the following implications: 1) employing higher order statistics from a curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images, as compared to other conventional wavelet transforms; 2) increasing the sensitivity and specificity of the system by the feature reduction phase; 3) realizing the system using an efficient classification engine, a neuro-C4.5 classifier, which provides better classification rate. An extensive experimental evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods.     

基于曲波变换和模糊理论的遥感图像融合

作为一种新的多尺度几何分析方法,曲波比小波更适于分析图像中的直线和曲线边缘特征,利用这一特性,提出了基于曲波变换和模糊理论的遥感图像融合算法,利用曲波变换获取代表待融合图像特征的曲波系数,并将模糊理论引入融合规则中,自适应确定曲波系数的加权权值,实现图像的融合处理.对SAR与全色、多光谱图像融合实验结果的分析表明,该算法的融合效果优于传统的基于PCA、小波变换以及标准曲波变换的融合算法.     

基于小波变换和曲波变换的图像边缘检测新算法

针对图像处理中的边缘检测问题,提出了一种基于小波变换和曲波变换的图像边缘检测新算法。首先对原始图像进行小波变换得到小波边缘图像;然后对原始图像进行曲波变换并使用Canny算子得到曲波边缘图像;最后基于小波变换的窗口内边缘强度自适应融合算法将小波边缘图像和曲波边缘图像进行融合得到最终边缘图像。该方法结合了小波变换描述图像细节特征的优势和曲波变换处理曲线或直线边缘特征的优势,能全面刻画边缘图像的纹理与细节信息,提高了图像清晰度。仿真实例表明了该算法的有效性。     

融合颜色词袋特征的视觉词汇树图像检索

针对由图像灰度空间产生的传统词袋模型SIFT特征无法体现图像的颜色信息的问题,提出了一种融合颜色特征的视觉词汇树来对图像进行描述。提取SIFT特征并建立词汇树,获取图像的SIFT表示向量。利用K-means方法对图像库中的所有图像的HSV值进行聚类,获得基于HSV空间的颜色词袋表示向量,避免了传统颜色直方图方法所带来的量化误差。将SIFT特征与颜色词袋特征进行融合,完成了图像的全局特征和局部特征的融合。然后,计算融合特征的相似度,将相似度从高到低排序,完成图像检索。为了验证本方法的有效性,选择Corel图像库对算法性能进行实验分析,从主观评价和客观评价标准分别进行评价,并与传统方法进行了对比。结果表明,特征融合的检索性能与单一特征方法相比有较大提高。特征融合方法的平均检索查准率和查全率-查准率等评价指标,对比传统方法均有不同程度提高。     

基于曲波活性测度的SAR与多光谱图像融合

曲波变换是一种更适合于图像处理的多尺度几何分析方法,具有很强的方向性。结合活性测度将其应用于合成孔径雷达(SAR)图像和多光谱(MS)图像融合可以更好地表示图像中的有用特征。首先,对多光谱图像和SAR图像的R、G、B三波段分别进行曲波变换,粗尺度系数采用3×3窗口系数活性测度进行融合,细节尺度直接取大,对粗尺度和细节尺度系数重构后得到最终融合结果。采用熵、平均梯度、信噪比和扭曲程度对融合结果进行评价。实验结果表明,基于曲波活性测度的融合方法在保持MS光谱信息和提高空间分辨率上都有较好的结果。     

Remote sensing image fusion using the curvelet transform

This paper presents an image fusion method suitable for pan-sharpening of multispectral (MS) bands, based on nonseparable multiresolution analysis (MRA). The low-resolution MS bands are resampled to the fine scale of the panchromatic (Pan) image and sharpened by injecting highpass directional details extracted from the high-resolution Pan image by means of the curvelet transform (CT). CT is a nonseparable MRA, whose basis functions are directional edges with progressively increasing resolution. The advantage of CT with respect to conventional separable MRA, either decimated or not, is twofold. Firstly, directional detail coefficients matching image edges may be preliminarily soft-thresholded to achieve a noise reduction that is better than that obtained in the separable wavelet domain. Secondly, modeling of the relationships between high-resolution detail coefficients of the MS bands and of the Pan image is more fitting, being accomplished in the directional multiresolution domain. Experiments are carried out on very-high-resolution MS + Pan images acquired by the QuickBird and Ikonos satellite systems. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed curvelet-based fusion method performs slightly better than the state-of-the art. Fusion tests at the full scale reveal that an accurate and reliable Pan-sharpening, little affected by local inaccuracies even in the presence of complex and detailed urban landscapes, is achieved by the proposed method.     

An endmember-based distance for content based hyperspectral image retrieval

We propose a specific content-based image retrieval (CBIR) system for hyperspectral images exploiting its rich spectral information. The CBIR image features are the endmember signatures obtained from the image data by endmember induction algorithms (EIAs). Endmembers correspond to the elementary materials in the scene, so that the pixel spectra can be decomposed into a linear combination of endmember signatures. EIA search for points in the high dimensional space of pixel spectra defining a convex polytope, often a simplex, covering the image data. This paper introduces a dissimilarity measure between hyperspectral images computed over the image induced endmembers, proving that it complies with the axioms of a distance. We provide a comparative discussion of dissimilarity functions, and quantitative evaluation of their relative performances on a large collection of synthetic hyperspectral images, and on a dataset extracted from a real hyperspectral image. Alternative dissimilarity functions considered are the Hausdorff distance and robust variations of it. We assess the CBIR performance sensitivity to changes in the distance between endmembers, the EIA employed, and some other conditions. The proposed hyperspectral image distance improves over the alternative dissimilarities in all quantitative performance measures. The visual results of the CBIR on the real image data demonstrate its usefulness for practical applications.     

第二代轮廓波纹理图像检索系统

提出了一种基于第二代轮廓波变换的纹理检索系统的实现方法。该系统采用第二代轮廓波变换域子带系数的能量和标准偏差序列作为特征向量,以Canberra距离为相似度度量标准。采用Brodatz纹理数据库中的109幅640×640的纹理图像为实验对象,比较了基于同样架构的第一代轮廓波变换和第二代轮廓波变换纹理检索系统的性能。实验结果表明：在特征向量长度、检索时间、所需存储空间基本相同的情况下,第二代轮廓波变换检索系统比第一代轮廓波变换检索系统具有更高的检索率。     

Interactive localized content based image retrieval with multiple-instance active learning

In this paper, we propose two general multiple-instance active learning (MIAL) methods, multiple-instance active learning with a simple margin strategy (S-MIAL) and multiple-instance active learning with fisher information (F-MIAL), and apply them to the active learning in localized content based image retrieval (LCBIR). S-MIAL considers the most ambiguous picture as the most valuable one, while F-MIAL utilizes the fisher information and analyzes the value of the unlabeled pictures by assigning different labels to them. In experiments, we will show their superior performances in LCBIR tasks.     

Combining spatial and colour information for content based image retrieval

Colour is one of the most important features in content based image retrieval. However, colour is rarely used as a feature that codes local spatial information, except for colour texture. This paper presents an approach to represent spatial colour distributions using local principal component analysis (PCA). The representation is based on image windows which are selected by two complementary data driven attentive mechanisms: a symmetry based saliency map and an edge and corner detector. The eigenvectors obtained from local PCA of the selected windows form colour patterns that capture both low and high spatial frequencies, so they are well suited for shape as well as texture representation. Projections of the windows selected from the image database to the local PCs serve as a compact representation for the search database. Queries are formulated by specifying windows within query images. System feedback makes both the search process and the results comprehensible for the user.     

A new matching strategy for content based image retrieval system

Adopting effective model to access the desired images is essential nowadays with the presence of a huge amount of digital images. The present paper introduces an accurate and rapid model for content based image retrieval process depending on a new matching strategy. The proposed model is composed of four major phases namely: features extraction, dimensionality reduction, ANN classifier and matching strategy. As for the feature extraction phase, it extracts a color and texture features, respectively, called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). However, integrating multiple features can overcome the problems of single feature, but the system works slowly mainly because of the high dimensionality of the feature space. Therefore, the dimensionality reduction technique selects the effective features that jointly have the largest dependency on the target class and minimal redundancy among themselves. Consequently, these features reduce the calculation work and the computation time in the retrieval process. The artificial neural network (ANN) in our proposed model serves as a classifier so that the selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image. In addition, the proposed model presents an effective feature matching strategy that depends on the idea of the minimum area between two vectors to compute the similarity value between a query image and the images in the determined class. Finally, the results presented in this paper demonstrate that the proposed model provides accurate retrieval results and achieve improvement in performance with significantly less computation time compared with other models.     

A hybrid multi-objective optimization algorithm for content based image retrieval

Relevance feedback methods in CBIR (Content Based Image Retrieval) iteratively use relevance information from the user to search the space for other relevant samples. As several regions of interest may be scattered through the space, an effective search algorithm should balance the exploration of the space to find new potential regions of interest and the exploitation of areas around samples which are known relevant. However, many algorithms concentrate the search on areas which are close to the images that the user has marked as relevant, according to a distance function in the (possibly deformed) multidimensional feature space. This maximizes the number of relevant images retrieved at the first iterations, but limits the discovery of new regions of interest and may leave unexplored a large section of the space. In this paper, we propose a novel hybrid approach that uses a scattered search algorithm based on NSGA II (Non-dominated Sorting Genetic Algorithm) only at the first iteration of the relevance feedback process, and then switches to an exploitation algorithm. The combined approach has been tested on three databases and in combination with several other methods. When the hybrid method does not produce better results from the first iteration, it soon catches up and improves both precision and recall.     

Adaptive tree similarity learning for image retrieval

Learning-enhanced relevance feedback is one of the most promising and active research directions in content-based image retrieval in recent years. However, the existing approaches either require prior knowledge of the data or converge slowly and are thus not coneffective. Motivated by the successful history of optimal adaptive filters, we present a new approach to interactive image retrieval based on an adaptive tree similarity model to solve these difficulties. The proposed tree model is a hierarchical nonlinear Boolean representation of a user query concept. Each path of the tree is a clustering pattern of the feedback samples, which is small enough and local in the feature space that it can be approximated by a linear model nicely. Because of the linearity, the parameters of the similartiy model are better learned by the optimal adaptive filter, which does not require any prior knowledge of the data and supports incremental learning with a fast convergence rate. The proposed approach is simple to implement and achieves better performance than most approaches. To illustrate the performance of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.An early version of part of the system was reported in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2001.     

A survey of browsing models for content based image retrieval

The problem of content based image retrieval (CBIR) has traditionally been investigated within a framework that emphasises the explicit formulation of a query: users initiate an automated search for relevant images by submitting an image or draw a sketch that exemplifies their information need. Often, relevance feedback is incorporated as a post-retrieval step for optimising the way evidence from different visual features is combined. While this sustained methodological focus has helped CBIR to mature, it has also brought out its limitations more clearly: There is often little support for exploratory search and scaling to very large collections is problematic. Moreover, the assumption that users are always able to formulate an appropriate query is questionable. An effective, albeit much less studied, method of accessing image collections based on visual content is that of browsing. The aim of this survey paper is to provide a structured overview of the different models that have been explored over the last one to two decades, to highlight the particular challenges of the browsing approach and to focus attention on a few interesting issues that warrant more intense research.