Texture classification and discrimination for region-based image retrieval

In RBIR, texture features are crucial in determining the class a region belongs to since they can overcome the limitations of color and shape features. Two robust approaches to model texture features are Gabor and curvelet features. Although both features are close to human visual perception, sufficient information needs to be extracted from their sub-bands for effective texture classification. Moreover, shape irregularity can be a problem since Gabor and curvelet transforms can only be applied on the regular shapes. In this paper, we propose an approach that uses both the Gabor wavelet and the curvelet transforms on the transferred regular shapes of the image regions. We also apply a fitting method to encode the sub-bands’ information in the polynomial coefficients to create a texture feature vector with the maximum power of discrimination. Experiments on texture classification task with ImageCLEF and Outex databases demonstrate the effectiveness of the proposed approach.     

A new texture image retrieval way

This paper proposes a new texture image retrieval method for the considering of the population search and random information exchange merits of evolving programming which can be used to optimize image feature vector extraction. The experimental results show that this way can efficiently improve the retrieval accuracy and realize fasttips with the advantage of evolving programming algorithm.     

最小差异采样的主动学习图像分类方法简

针对委员会成员模型投票不一致性的度量问题,提出了一种基于最小差异采样的主动学习图像分类方法。该方法首先基于标注样本集的重采样结果构建决策委员会,然后利用投票概率较高的2个类别的概率值的差异来度量未标注样本集每个样本的投票不一致性,选择概率差异最小的样本交由人工专家标注,如此迭代更新分类器。将新方法与EQB算法及nEQB算法在多个数据集上进行实验对比,实验结果表明所提方法能够有效提高分类的准确率。还对组成决策委员会的成员模型的数目设置进行了分析和讨论,结果表明在相同的成员模型数目时所提方法比nEQB算法更为有效。     

A new approach in content-based image retrieval using fuzzy

Finding an image from a large set of images is an extremely difficult problem. One solution is to label images manually, but this is very expensive, time consuming and infeasible for many applications. Furthermore, the labeling process depends on the semantic accuracy in describing the image. Therefore many Content based Image Retrieval (CBIR) systems are developed to extract low-level features for describing the image content. However, this approach decreases the human interaction with the system due to the semantic gap between low-level features and high-level concepts. In this study we make use of fuzzy logic to improve CBIR by allowing users to express their requirements in words, the natural way of human communication. In our system the image is represented by a Fuzzy Attributed Relational Graph (FARG) that describes each object in the image, its attributes and spatial relation. The texture and color attributes are computed in a way that model the Human Vision System (HSV). We proposed a new approach for graph matching that resemble the human thinking process. The proposed system is evaluated by different users with different perspectives and is found to match users’ satisfaction to a high degree.     

Feature discovering for image classification via wavelet-like pattern decomposition

In this paper, we propose a feature discovering method incorporated with a wavelet-like pattern decomposition strategy to address the image classification problem. In each level, we design a discriminative feature discovering dictionary learning (DFDDL) model to exploit the representative visual samples from each class and further decompose the commonality and individuality visual patterns simultaneously. The representative samples reflect the discriminative visual cues per class, which are beneficial for the classification task. Furthermore, the commonality visual elements capture the communal visual patterns across all classes. Meanwhile, the class-specific discriminative information can be collected by the learned individuality visual elements. To further discover the more discriminative feature information from each class, we then integrate the DFDDL into a wavelet-like hierarchical architecture. Due to the designed hierarchical strategy, the discriminative power of feature representation can be promoted. In the experiment, the effectiveness of proposed method is verified on the challenging public datasets.     

A new method for image retrieval based on analyzing fractal coding characters

It has been effectively proved that histogram of image fractal coding parameters can be used for image retrieval. In recent years, many researchers have paid more and more attention to this application of image fractal coding. In this paper, a new statistical method, based on kernel density estimation, is used for analyzing fractal coding parameters. The fractal signatures are then extracted for texture image retrieval. Experimental results show that the proposed method not only has higher retrieval rate but also faster retrieval speed than existing method.     

A new SVM based emotional classification of image

How high-level emotional representation of art paintings can be inferred from perceptual level features suited for the particular classes (dynamic vs. static classification) is presented. The key points are feature selection and classification. According to the strong relationship between notable lines of image and human sensations, a novel feature vector WLDLV (Weighted Line Direction-Length Vector) is proposed, which includes both orientation and length information of lines in an image. Classification is performed by SVM (Support Vector Machine) and images can be classified into dynamic and static. Experimental results demonstrate the effectiveness and superiority of the algorithm.     

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。     

A new image retrieval model based on monogenic signal representation

This paper proposes a novel image retrieval model based on monogenic signal representation. An original image is decomposed into three complementary components: amplitude, orientation and phase by monogenic signal representation. The monogenic variation in each local region and monogenic feature in each pixel are encoded, and then the statistical features of the local features encoded are calculated. In order to overcome the problem of high feature dimensionality, the local statistical features extracted from the complementary monogenic components are projected by block-based fisher discriminant analysis, which not only reduces the dimensionality of the features extracted, but also enhances its discriminative power. Finally, these features reduced are fused for effective image retrieval. Experimental results show that our scheme can effectively describe an image, and obviously improve the average retrieval precision.     

一种有效的基于内容的图像检索方法

本文针对基于内容的图像检索中特征和相似度问题，提出新的距离模式，并以彩色空间中扩展共发矩阵作为纹理描述，在测试系统iPhoto上，数据库为56600幅图像时，实验结果显示，本文方法优于传统方法。     

A new eigenstructure method for sinusoidal signal retrieval inwhite noise: estimation and pattern recognition

A new approach, in a framework of an eigenstructure method using a Hankel matrix, is developed for sinusoidal signal retrieval in white noise. A closed-form solution for the singular pairs of the matrix is defined in terms of the associated sinusoidal signals and noise. The estimated sinusoidal singular vectors are applied to form the noise-free Hankel matrix. A pattern recognition technique is proposed for partitioning signal and noise subspaces based on the singular pairs of the Hankel matrix. Three types of cluster structures in an eigen-spectrum plot are identified: well-separated, touching, and overlapping. The overlapping, which is the most difficult case, corresponds to a low signal-to noise ratio (SNR). Optimization of Hankel matrix dimensions is suggested for enhancing separability of cluster structures. Once features have been extracted from both singular value and singular vector data, a fuzzy classifier is used to identify each singular component. Computer simulations have shown that the method is effective for the case of “touching” data and provides reasonably good results for a sinusoidal signal reconstruction in the time domain. The limitations of the method are also discussed     

Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking

A new algorithm meant for content based image retrieval (CBIR) and object tracking applications is presented in this paper. The local region of image is represented by local maximum edge binary patterns (LMEBP), which are evaluated by taking into consideration the magnitude of local difference between the center pixel and its neighbors. This LMEBP differs from the existing LBP in a manner that it extracts the information based on distribution of edges in an image. Further, the effectiveness of our algorithm is confirmed by combining it with Gabor transform. Four experiments have been carried out for proving the worth of our algorithm. Out of which three are meant for CBIR and one for object tracking. It is further mentioned that the database considered for first three experiments are Brodatz texture database (DB1), MIT VisTex database (DB2), rotated Brodatz database (DB3) and the fourth contains three observations. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and other existing transform domain techniques.     

Center symmetric local binary co-occurrence pattern for texture,face and bio-medical image retrieval

Content based image retrieval is a common problem for a large image database. Many methods have been proposed for image retrieval for some particular type of datasets. In the proposed work, a new image retrieval technique has been introduced. This technique is useful for different kind of dataset. In the proposed method, center symmetric local binary pattern has been extracted from the original image to obtain the local information. Co-occurrence of pixel pairs in local pattern map have been observed in different directions and distances using gray level co-occurrence matrix. Earlier methods have utilized histogram to extract the frequency information of local pattern map but co-occurrence of pixel pairs is more robust than frequency of patterns. The proposed method is tested on three different category of images, i.e., texture, face and medical image database and compared with typical state-of-the-art local patterns.     

Feature selection for image retrieval

A new image retrieval method based on dynamical interesting features selection is proposed. The method uses the Fisher discriminant criterion to dynamically select optimal interesting features from the images and uses them to retrieve the similar images in the database. Experimental results show that it has better retrieval performance than the DPF based and classical Minkowski distance based methods.     

结合金字塔和局部二值模式的遥感图像分类

在住宅区遥感图像分类中,为了克服尺度变化和旋转变化带来的影响,提出一种结合金字塔原理和局部二值模式的图像分类算法。首先对原始住宅区遥感图像进行多次下采样以构建不同尺度的空间金字塔;然后利用局部二值模式提取不同尺度遥感图像的纹理特征,以消除旋转变化的影响;最后将不同尺度下的纹理特征融合到一起,利用支持向量机对住宅区遥感图像进行分类。在标准图像数据集上的实验结果表明,低尺度纹理特征将会降低住宅区遥感图像的分类精度,与单尺度纹理特征相比,多尺度融合的纹理特征提高了遥感图像分类精度,平均高达4.77%。     

A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel function

Relevance feedback (RF) is an effective approach to bridge the gap between low-level visual features and high-level semantic meanings in content-based image retrieval (CBIR). The support vector machine (SVM) based RF mechanisms have been used in different fields of image retrieval, but they often treat all positive and negative feedback samples equally, which will inevitably degrade the effectiveness of SVM-based RF approaches for CBIR. In fact, positive and negative feedback samples, different positive feedback samples, and different negative feedback samples all always have distinct properties. Moreover, each feedback interaction process is usually tedious and time-consuming because of complex visual features, so if too many times of iteration of feedback are asked, users may be impatient to interact with the CBIR system. To overcome the above limitations, we propose a new SVM-based RF approach using probabilistic feature and weighted kernel function in this paper. Firstly, the probabilistic features of each image are extracted by using principal components analysis (PCA) and the adapted Gaussian mixture models (AGMM) based dimension reduction, and the similarity is computed by employing Kullback–Leibler divergence. Secondly, the positive feedback samples and negative feedback samples are marked, and all feedback samples’ weight values are computed by utilizing the samples-based Relief feature weighting. Finally, the SVM kernel function is modified dynamically according to the feedback samples’ weight values. Extensive simulations on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches.     

A novel local octa-pattern feature descriptor for image retrieval

The local tetra patterns (LTrPs) gives four-directional information and ignores the diagonal pixel information, thereby affecting the retrieved image efficiency. In the present work, a novel retrieval approach has been proposed using local octa-patterns (LOcPs) for content-based image indexing and retrieval. The proposed approach encodes the center pixel directional information with its eight adjacent neighbors, from the directions that are computed using the first-order derivatives. Also the nth-order LOcP is computed using \((n-1)\)th-order local direction variations. In addition, the performance of the developed method by combining it with the Gabor transform has been analyzed. The performance of the proposed technique has been compared to existing techniques like LBP, LTP, LDP, and LTrP on Corel-1000 database (DB1) and Describable Textures Dataset (DB2). The performance observed shows that the developed method improves the retrieval parameters from 75.9%/77.13% to 79.4%/81.5% in the form of average precision on DB1/DB2 databases.