基于切比雪夫矩无参考模糊图像质量评价

提出了一种新的基于Tchebichef矩的无参考模糊图像质量评价方法。将模糊图像通过低通滤波得到再模糊图像;将模糊图像和再模糊图像分别进行8×8分块并计算每一图像块的Tchebichef矩;根据Tchebichef矩块的值将原始图像块分为平滑块,纹理块和边缘块,计算原始图像和再模糊图像对应块之间的Tchebichef矩向量相似度,得到三类图像块的局部平均相似度;进行融合得到原始图像的最终评价质量。实验结果表明,该方法优于其他算法,与主观评分有更好的一致性,能够更准确地评价模糊图像质量。     

A novel similarity based quality metric for image fusion

A novel objective quality metric for image fusion is presented. The interest of our metric lies in the fact that the redundant regions and the complementary/conflicting regions are treated respectively according to the structural similarity between the source images. The experiments show that the proposed measure is consistent with human visual evaluations and can be applied to evaluate image fusion schemes that are not performed at the same level.     

No-reference image quality assessment using fusion metric

This paper presents a fusion featured metric for no-reference image quality assessment of natural images. Natural images exhibit strong statistical properties across the visual contents such as leading edge, high dimensional singularity, scale invariance, etc. The leading edge represents the strong presence of continuous points, whereas high singularity conveys about non-continuous points along the curves. Both edges and curves are equally important in perceiving the natural images. Distortions to the image affect the intensities of these points. The change in the intensities of these key points can be measured using SIFT. However, SIFT tends to ignore certain points such as the points in the low contrast region which can be identified by curvelet transform. Therefore, we propose a fusion of SIFT key points and the points identified by curvelet transform to model these changes. The proposed fused feature metric is computationally efficient and light on resources. The neruofuzzy classifier is employed to evaluate the proposed feature metric. Experimental results show a good correlation between subjective and objective scores for public datasets LIVE, TID2008, and TID2013.

     

SWVFS: a saliency weighted visual feature similarity metric for image quality assessment

In this paper, a saliency weighted visual feature similarity (SWVFS) metric is proposed for full reference image quality assessment (IQA). Instead of traditional spatial pooling strategies, a visual saliency-based approach is employed for better compliance with properties of the human visual system, where the saliency allocation is closely related to the activity of posterior parietal cortex and the pluvial nuclei of the thalamus. Assuming that the saliency map actually represents the contribution of locally computed visual distortions to the overall image quality, the gradient similarity and the textural congruency are merged into the final image quality indicator. The gradient and texture comparison play complementary roles in characterizing the local image distortion. Extensive experiments conducted on seven publicly available image databases show that the performance of SWVFS is competitive with the state-of-the-art IQA algorithms.     

MetricFusion: Generalized metric swarm learning for similarity measure

Learning distance metrics for measuring the similarity between two data points in unsupervised and supervised pattern recognition has been widely studied in unconstrained face verification tasks. Motivated by the fact that enforcing single distance metric learning for verification via an empirical score threshold is not robust in uncontrolled experimental conditions, we therefore propose to obtain a metric swarm by learning local patches alike sub-metrics simultaneously that naturally formulates a generalized metric swarm learning (GMSL) model with a joint similarity score function solved by an efficient alternative optimization algorithm. Further, each sample pair is represented as a similarity vector via the well-learned metric swarm, such that the face verification task becomes a generalized SVM-alike classification problem. Therefore, the verification can be enforced in the represented metric swarm space that can well improve the robustness of verification under irregular data structure. Experiments are preliminarily conducted using several UCI benchmark datasets for solving general classification problem. Further, the face verification experiments on real-world LFW and PubFig datasets demonstrate that our proposed model outperforms several state-of-the-art metric learning methods.     

Content-based image retrieval using joint correlograms

The comparison of digital images to determine their degree of similarity is one of the fundamental problems of computer vision. Many techniques exist which accomplish this with a certain level of success, most of which involve either the analysis of pixel-level features or the segmentation of images into sub-objects that can be geometrically compared. In this paper we develop and evaluate a new variation of the pixel feature and analysis technique known as the color correlogram in the context of a content-based image retrieval system. Our approach is to extend the autocorrelogram by adding multiple image features in addition to color. We compare the performance of each index scheme with our method for image retrieval on a large database of images. The experiment shows that our proposed method gives a significant improvement over histogram or color correlogram indexing, and it is also memory-efficient.

SVD-based quality metric for image and video using machine learning

We study the use of machine learning for visual quality evaluation with comprehensive singular value decomposition (SVD)-based visual features. In this paper, the two-stage process and the relevant work in the existing visual quality metrics are first introduced followed by an in-depth analysis of SVD for visual quality assessment. Singular values and vectors form the selected features for visual quality assessment. Machine learning is then used for the feature pooling process and demonstrated to be effective. This is to address the limitations of the existing pooling techniques, like simple summation, averaging, Minkowski summation, etc., which tend to be ad hoc. We advocate machine learning for feature pooling because it is more systematic and data driven. The experiments show that the proposed method outperforms the eight existing relevant schemes. Extensive analysis and cross validation are performed with ten publicly available databases (eight for images with a total of 4042 test images and two for video with a total of 228 videos). We use all publicly accessible software and databases in this study, as well as making our own software public, to facilitate comparison in future research.     

基于内容的多特征融合图像检索

提出一种组合颜色、形状和空间信息的图像检索方法,用颜色块的颜色直方图表示图像的颜色特征;使用Zernike矩表示图像的形状特征,通过提取颜色块的质心、分布方差聚散度等特征得到图像的空间特征。为了进一步提高图像检索的精确度,提出一种基于支持向量机的相关反馈方法。实验结果表明,基于组合特征的图像检索方法优于基于单一特征的检索方法。     

Perceptual image quality mutual information assessment metric using of Gabor features

A good objective metric of image quality assessment （IQA） should be consistent with the subjective judgment of human beings. In this paper, a four-stage perceptual approach for full reference IQA is presented. In the first stage, the visual features are extracted by 2-D Gabor filter that has the excellent performance of modeling the receptive fields of simple cells in the primary visual cortex. Then in the second stage, the extracted features are post-processed by the divisive normalization transform to reflect the nonlinear mechanisms in human visual systems. In the third stage, mutual information between the visual features of the reference and distorted images is employed to measure the visual quality. And in the last pooling stage, the mutual information is converted to the final objective quality score. Experimental results show that the proposed metic has a high correlation with the subjective assessment and outperforms other state-of-the-art metrics.     

Perceptual image quality assessment metric using mutual information of Gabor features

A good objective metric of image quality assessment (IQA) should be consistent with the subjective judgment of human beings. In this paper, a four-stage perceptual approach for full reference IQA is presented. In the first stage, the visual features are extracted by 2-D Gabor filter that has the excellent performance of modeling the receptive fields of simple cells in the primary visual cortex. Then in the second stage, the extracted features are post-processed by the divisive normalization transform to reflect the nonlinear mechanisms in human visual systems. In the third stage, mutual information between the visual features of the reference and distorted images is employed to measure the visual quality. And in the last pooling stage, the mutual information is converted to the final objective quality score. Experimental results show that the proposed metic has a high correlation with the subjective assessment and outperforms other state-of-the-art metrics.     

Content-based facial image retrieval using common vector

Abstract： Facial image retrieval is an essential application of content-based image retrieval. Based on the analysis of the practical application background, this paper proposes a new facial image retrieval scheme. In this scheme, the input query image is firstly transformed by four different methods to generate virtual samples and enlarge the training set. Moreover common vector method is applied to span the feature space for the training set whose images just belong to one class. To prove the feasibility of the scheme, a series of experiments are performed on the ORL face database.     

Content-based image retrieval using fuzzy perceptual feedback

In this paper, a new framework called fuzzy relevance feedback in interactive content-based image retrieval (CBIR) systems is introduced. Conventional binary labeling scheme in relevance feedback requires a crisp decision to be made on the relevance of the retrieved images. However, it is inflexible as user interpretation of visual content varies with respect to different information needs and perceptual subjectivity. In addition, users tend to learn from the retrieval results to further refine their information requests. It is, therefore, inadequate to describe the user’s fuzzy perception of image similarity with crisp logic. In view of this, we propose a fuzzy relevance feedback approach which enables the user to make a fuzzy judgement. It integrates the user’s fuzzy interpretation of visual content into the notion of relevance feedback. An efficient learning approach is proposed using a fuzzy radial basis function (FRBF) network. The network is constructed based on the user’s feedbacks. The underlying network parameters are optimized by adopting a gradient-descent training strategy due to its computational efficiency. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed method.

Content-based image indexing and searching using Daubechies' wavelets

This paper describes WBIIS (Wavelet-Based Image Indexing and Searching), a new image indexing and retrieval algorithm with partial sketch image searching capability for large image databases. The algorithm characterizes the color variations over the spatial extent of the image in a manner that provides semantically meaningful image comparisons. The indexing algorithm applies a Daubechies' wavelet transform for each of the three opponent color components. The wavelet coefficients in the lowest few frequency bands, and their variances, are stored as feature vectors. To speed up retrieval, a two-step procedure is used that first does a crude selection based on the variances, and then refines the search by performing a feature vector match between the selected images and the query. For better accuracy in searching, two-level multiresolution matching may also be used. Masks are used for partial-sketch queries. This technique performs much better in capturing coherence of image, object granularity, local color/texture, and bias avoidance than traditional color layout algorithms. WBIIS is much faster and more accurate than traditional algorithms. When tested on a database of more than 10 000 general-purpose images, the best 100 matches were found in 3.3 seconds.     

基于度量直方图的医学图像检索

研究了医学图像灰度分布的特性,利用相邻灰度的相关性提出了基于度量直方图的医学图像检索方法及其度量空间上的距离函数MHD0;该方法减少了传统直方图特征的维教,克服了SAM索引的缺点。通过对CT图像数据库的检索实验,验证了该方法在性能和速度上都超过了传统直方图检索方法。     

Multiple-instance content-based image retrieval employing isometric embedded similarity measure

In image-based retrieval, global or local features sufficiently discriminative to summarize the image content are commonly extracted first. Traditional features, such as color, texture, shape or corner, characterizing image content are not reliable in terms of similarity measure. A good match in the feature domain does not necessarily map to image pairs with similar relationship. Applying these features as search keys may retrieve dissimilar false-positive images, or leave similar false-negative ones behind. Moreover, images are inherently ambiguous since they contain a great amount of information that justifies many different facets of interpretation. Using a single image to query a database might employ features that do not match user's expectation and retrieve results with low precision/recall ratios. How to automatically extract reliable image features as a query key that matches user's expectation in a content-based image retrieval (CBIR) system is an important topic.The objective of the present work is to propose a multiple-instance learning image retrieval system by incorporating an isometric embedded similarity measure. Multiple-instance learning is a way of modeling ambiguity in supervised learning given multiple examples. From a small collection of positive and negative example images, semantically relevant concepts can be derived automatically and employed to retrieve images from an image database. Each positive and negative example images are represented by a linear combination of fractal orthonormal basis vectors. The mapping coefficients of an image projected onto each orthonormal basis constitute a feature vector. The Euclidean-distance similarity measure is proved to remain consistent, i.e., isometric embedded, between any image pairs before and after the projection onto orthonormal axes. Not only similar images generate points close to each other in the feature space, but also dissimilar ones produce feature points far apart.The utilization of an isometric-embedded fractal-based technique to extract reliable image features, combined with a multiple-instance learning paradigm to derive relevant concepts, can produce desirable retrieval results that better match user's expectation. In order to demonstrate the feasibility of the proposed approach, two sets of test for querying an image database are performed, namely, the fractal-based feature extraction algorithm vs. three other feature extractors, and single-instance vs. multiple-instance learning. Both the retrieval results, execution time and precision/recall curves show favorably for the proposed multiple-instance fractal-based approach.     

Content-based image collection summarization and comparison using self-organizing maps

Progresses made on content-based image retrieval have reactivated the research on image analysis and a number of similarity-based methods have been established to assess the similarity between images. In this paper, the content-based approach is extended towards the problem of image collection summarization and comparison. For these purposes we propose to carry out clustering analysis on visual features using self-organizing maps, and then evaluate their similarity using a few dissimilarity measures implemented on the feature maps. The effectiveness of these dissimilarity measures is then examined with an empirical study.     

Content-based image retrieval with relevance feedback using random walks

In this paper, we propose a novel approach to content-based image retrieval with relevance feedback, which is based on the random walker algorithm introduced in the context of interactive image segmentation. The idea is to treat the relevant and non-relevant images labeled by the user at every feedback round as “seed” nodes for the random walker problem. The ranking score for each unlabeled image is computed as the probability that a random walker starting from that image will reach a relevant seed before encountering a non-relevant one. Our method is easy to implement, parameter-free and scales well to large datasets. Extensive experiments on different real datasets with several image similarity measures show the superiority of our method over different recent approaches.     

Content-based facial image retrieval using constrained independent component analysis

Content-based image retrieval (CBIR) is a method of searching, browsing, and querying images according to their content. In this paper, we focus on a specific domain of CBIR that involves the development of a content-based facial image retrieval system based on the constrained independent component analysis (cICA). Originating from independent component analysis (ICA), cICA is a source separation technique that uses priori constraints to extract desired independent components (ICs) from data. By providing query images as the constraints to the cICA, the ICs that share similar probabilistic features with the queries from the database can be extracted. Then, these extracted ICs are used to evaluate the rank of each image according to the query. In our approach, we demonstrate that, in addition to a single image-based query, a compound query with multiple query images can be used to search for images with compounding feature content. The experimental results of our CBIR system tested with different facial databases show that our system can improve retrieval performance by using a compound query. Furthermore, our system allows for online processing without the need to learn query images.