A retrieval system to analyse dermatological lesions using feature ortho-normalisation

An information retrieval system is proposed as an assistance tool for diagnosing the skin lesion using Content-Based Image Retrieval approach. Efficiency of the retrieval system is deliberated in terms of the most relevant retrieval of images from database. The proposed diagnostic assistive model retrieves the skin lesion images and its disease category, case history, symptoms and treatment plan. This retrieval process is made from a dermatology database by the way of visual features in the input image such as shape, texture and colour. The author’s proposed principal component analysis (PCA) feature projection technique is to discriminate the features by projecting them onto a feature subspace. While projecting the features onto a feature subspace features are normalised orthogonally. So the proposed methodology is used to improve the classification by the way of discriminate the features, in-turn it focus the retrieval of comprehensive reference sources, so that the diagnosis accuracy of the dermatologists are also improved. Receiver-operating characteristic curve is used to analyse the proposed computer-aided diagnosis (CAD) method, while analysis we attained high contribution to detect the skin lesions. Totally 1450 images are experimented and the system produced the 99.09% specificity, 96.69% sensitivity and 98.3% accuracy. When compared with other works this system of assessment shows high retrieval and diagnosis concert.     

Learning effective color features for content based image retrieval in dermatology

We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn favorable feature representations for this special application: limited rank matrix learning vector quantization (LiRaM LVQ) and a Large Margin Nearest Neighbor (LMNN) approach. Both methods use labeled training data and provide a discriminant linear transformation of the original features, potentially to a lower dimensional space. The extracted color features are used to retrieve images from a database by a k-nearest neighbor search. We perform a comparison of retrieval rates achieved with extracted and original features for eight different standard color spaces. We achieved significant improvements in every examined color space. The increase of the mean correct retrieval rate lies between 10% and 27% in the range of k=1-25 retrieved images, and the correct retrieval rate lies between 84% and 64%. We present explicit combinations of RGB and CIE-Lab color features corresponding to healthy and lesion skin. LiRaM LVQ and the computationally more expensive LMNN give comparable results for large values of the method parameter κ of LMNN (κ≥25) while LiRaM LVQ outperforms LMNN for smaller values of κ. We conclude that feature extraction by LiRaM LVQ leads to considerable improvement in color-based retrieval of dermatologic images.     

Extraction of major object features using VQ clustering for content-based image retrieval

An image representation method using vector quantization (VQ) on color and texture is proposed in this paper. The proposed method is also used to retrieve similar images from database systems. The basic idea is a transformation from the raw pixel data to a small set of image regions, which are coherent in color and texture space. A scheme is provided for object-based image retrieval. Features for image retrieval are the three color features (hue, saturation, and value) from the HSV color model and five textural features (ASM, contrast, correlation, variance, and entropy) from the gray-level co-occurrence matrices. Once the features are extracted from an image, eight-dimensional feature vectors represent each pixel in the image. The VQ algorithm is used to rapidly cluster those feature vectors into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to the object within the image. This method can retrieve similar images even in cases where objects are translated, scaled, and rotated.     

综合颜色和形状特征聚类的图像检索

为了提高图像检索的速度和准确率,通过分析各种聚类算法在图像检索中的缺点,提出了一种新的划分聚类的图像检索方法。首先对HSV模型非均匀量化,利用改进的颜色聚合向量方法提取图像的颜色特征;然后基于改进的Hu不变矩提取图像的全局形状特征;最后,综合颜色和形状特征对图像基于贡献度聚类并建立特征索引库。利用上述方法在Corel图像库中进行图像检索。实验结果表明,与改进的K-means算法的图像检索算法相比,提出算法的查准率和查全率均有较大提高。     

基于二值信息的颜色和形状特征的图像检索

由于单一特征不足以准确地描述图像,提出了一种结合颜色、形状特征的图像检索方法.提出了新的用二值信息来表示图像的主色、全局色和形状特征的方法,并由此特征构造两个过滤器快速地过滤图像库中明显不相同的图像,以提高检索速度;采用改进的颜色直方图和形状基本特征进行相似度计算,为进一步提高图像检索的质量引入相关反馈机制,提出了一种动态调整两幅图像相似度中颜色特征和形状特征的权值系数的方法.文中方法与其它方法进行了比较实验,结果表明,该方法优于其它方法.     

基于多特征高效索引的图像检索

为了提高图像检索的准确率和速度,提出了一种多特征组合的图像检索算法。在颜色空间非均匀量化的基础上,利用改进的颜色聚合向量方法提取图像的颜色特征;基于改进的灰度共生矩阵提取纹理特征参数;利用Krawtchouk矩不变量提取图像的形状特征;基于贡献度聚类并建立特征索引库。融合上述特征计算图像间的相似度,使用特征索引对图像进行快速检索。实验结果表明,提出算法的检索精度有较大提高,能快速检索出用户所需的图像。     

形状的全尺度可视化表示与识别

目的 视觉目标的形状特征表示和识别是图像领域中的重要问题。在实际应用中,视角、形变、遮挡和噪声等干扰因素造成识别精度较低,且大数据场景需要算法具有较高的学习效率。针对这些问题,本文提出一种全尺度可视化形状表示方法。方法 在尺度空间的所有尺度上对形状轮廓提取形状的不变量特征,获得形状的全尺度特征。将获得的全部特征紧凑地表示为单幅彩色图像,得到形状特征的可视化表示。将表示形状特征的彩色图像输入双路卷积网络模型,完成形状分类和检索任务。结果 通过对原始形状加入旋转、遮挡和噪声等不同干扰的定性实验,验证了本文方法具有旋转和缩放不变性,以及对铰接变换、遮挡和噪声等干扰的鲁棒性。在通用数据集上进行形状分类和形状检索的定量实验,所得准确率在不同数据集上均超过对比算法。在MPEG-7数据集上精度达到99.57%,对比算法的最好结果为98.84%。在铰接和射影变换数据集上皆达到100%的识别精度,而对比算法的最好结果分别为89.75%和95%。结论 本文提出的全尺度可视化形状表示方法,通过一幅彩色图像紧凑地表达了全部形状信息。通过卷积模型既学习了轮廓点间的形状特征关系,又学习了不同尺度间的形状特征关系。本文方法在视角变化、局部遮挡、铰接变形和噪声等干扰下能保持较高的识别正确率,可应用于图像采集干扰较多以及红外或深度图像的目标识别,并适用于大数据场景下的识别任务。     

基于渲染图像角度结构特征的三维模型检索方法

为了充分利用三维模型的颜色、形状、纹理等特征,提出以三维模型渲染图像为数据集,利用渲染图像角度结构特征实现三维模型检索。首先,该方法以三维模型渲染图像为测试集,利用已有类别标记的自然图像作为训练集,通过骨架形状上下文特征对渲染图像进行分类,提取角度结构特征,建立特征库;然后,对输入的自然图像提取角度结构特征,与特征库中的角度结构特征进行相似度匹配计算,实现三维模型检索。实验结果表明, 充分利用 渲染图像的颜色、形状和空间信息是实现三维模型检索的有效方法。     

基于BoC-BoF特征的图像检索方法研究

为了优化基于内容的图像检索方法,提出了一种融合特征来表征图像内容.首先,提取基于RootSift描述子的特征词袋(Bag-of-Features,BoF)表示向量,获得图像的边缘和形状信息;其次,采用基于HSV的颜色词袋(Bag-of-Colors,BoC)表示向量来代替传统颜色直方图方法,获取图像的颜色信息;最后,将BoF表示向量和BoC表示向量相融合,形成BoC-BoF特征向量.BoC-BoF特征有效地实现了全局特征和局部特征的融合.两个数据集检索的实验结果表明,该方法比其它方法更加有效.     

Indexing shapes in image databases using the centroid–radii model

In content-based image retrieval systems, the content of an image such as color, shapes and textures are used to retrieve images that are similar to a query image. Most of the existing work focus on the retrieval effectiveness of using content for retrieval, i.e., study the accuracy (in terms of recall and precision) of using different representations of content. In this paper, we address the issue of retrieval efficiency, i.e., study the speed of retrieval, since a slow system is not useful for large image databases. In particular, we look at using the shape feature as the content of an image, and employ the centroid–radii model to represent the shape feature of objects in an image. This facilitates multi-resolution and similarity retrievals. Furthermore, using the model, the shape of an object can be transformed into a point in a high-dimensional data space. We can thus employ any existing high-dimensional point index as an index to speed up the retrieval of images. We propose a multi-level R-tree index, called the Nested R-trees (NR-trees) and compare its performance with that of the R-tree. Our experimental study shows that NR-trees can reduce the retrieval time significantly compared to R-tree, and facilitate similarity retrieval. We note that our NR-trees can also be used to index high-dimensional point data commonly found in many other applications.     

形状和颜色混合不变量在图像检索中的应用

多视觉特征的图像检索是当前基于内容的图像检索领域的重要方向.已有的多特征的检索主要通过线性加权的方法对特征进行组合,但这种组合方式仅实现了代数意义上的合并,未能真正利用和发掘特征间存在的相互关系,并且权重值不容易确定,检索结果易受权重值的影响.针对这一问题,提出一种形状-颜色混合不变特征的构造方法,特征提取的过程包含同时对形状、颜色信息的抽取,直接构造出能够同时对形状仿射变换和颜色对角-偏移变换具有不变性的特征,也称作形状-颜色矩不变量.首先分别在图像的2维几何空间、3维颜色空间定义形状核、颜色核,然后对形状核、颜色核的乘积进行多重积分,最后做规范化,就得到一个不变量.理论上,通过选择不同的形状核、颜色核可以推导出无穷多的不变量.实验结果表明,该方法优于加权组合特征的方法;与加权特征、局部特征相比,形状-颜色矩不变量对于同一物体不同成像条件下的近复制图像、整体属性相似的图像、大体类似的物体图像等表现出较高的检索性能及效率.     

基于SVM的图像低层特征与高层语义的关联

在基于内容的图像检索中，针对图像的低层可视特征与高层语义特征之间的鸿沟，提出了一种基于支持向量机（SVM）的语义关联方法。通过对图像低层特征的分析，提取了颜色和形状特征向量（221维），将它们作为支持向量机的输入向量，对图像类进行学习，建立图像低层特征与高层语义的关联，并应用于鸟类、花卉、海洋以及建筑物等几个典型的语义类别检索。实验结果表明，该方法可适应于不同用户的图像检索，并提高了检索性能。     

Feed-forward content based image retrieval using adaptive tetrolet transforms

This paper proposes a new approach for content based image retrieval based on feed-forward architecture and Tetrolet transforms. The proposed method addresses the problems of accuracy and retrieval time of the retrieval system. The proposed retrieval system works in two phases: feature extraction and retrieval. The feature extraction phase extracts the texture, edge and color features in a sequence. The texture features are extracted using Tetrolet transform. This transform provides better texture analysis by considering the local geometry of the image. Edge orientation histogram is used for retrieving the edge feature while color histogram is used for extracting the color features. Further retrieval phase retrieves the images in the feed-forward manner. At each stage, the number of images for next stage is reduced by filtering out irrelevant images. The Euclidean distance is used to measure the distance between the query and database images at each stage. The experimental results on COREL- 1 K and CIFAR - 10 benchmark databases show that the proposed system performs better in terms of the accuracy and retrieval time in comparison to the state-of-the-art methods.     

基于目标区域的语义图像检索

提出了一种基于高层语义的图像检索方法,该方法首先将图像分割成区域,提取每个区域的颜色、形状、位置特征,然后使用这些特征对图像对象进行聚类,得到每幅图像的语义特征向量;采用模糊C均值算法对图像进行聚类,在图像检索时,查询图像和聚类中心比较,然后在距离最小的类中进行检索。实验表明,提出的方法可以明显提高检索效率,缩小低层特征和高层语义之间的“语义鸿沟”。