基于内容的图像检索相关反馈算法的改进

基于内容的图像检索研究(Content-based Image Retrieval, CBIR)的目的是实现自动地、智能地检索图像,研究的对象是使查询者可以方便、快速、准确地从图像数据库中查找特定图像的方法和技术.本文在改进传统的相关反馈算法基础上,引入可更新的特征库,可以将用户反馈的信息逐步嵌入到这个可更新特征库中.实验结果证实了本文改进算法的有效性.     

Localized content-based image retrieval

We define localized content-based image retrieval as a CBIR task where the user is only interested in a portion of the image, and the rest of the image is irrelevant. In this paper we present a localized CBIR system, Accio, that uses labeled images in conjunction with a multiple-instance learning algorithm to first identify the desired object and weight the features accordingly, and then to rank images in the database using a similarity measure that is based upon only the relevant portions of the image. A challenge for localized CBIR is how to represent the image to capture the content. We present and compare two novel image representations, which extend traditional segmentation-based and salient point-based techniques respectively, to capture content in a localized CBIR setting.     

图像检索中的动态相似性度量方法

为提高图像检索的效率,近年来相关反馈机制被引入到了基于内容的图像检索领域。该文提出了一种新的相关反馈方法－－动态相似性度量方法。该方法建立在目前被广泛采用的图像相拟性度量方法的基础上,结合了相关反馈图像检索系统的时序特性,通过捕获用户的交互信息,动态地修正图像的相似性度量公式,从而把用户模型嵌入到了图像检索系统,在某种程度上使图像检索结果与人的主观感知更加接近。实验结果表明该方法的性能明显优于其它图像检索系统所采用的方法。     

Self organizing natural scene image retrieval

In this work we describe a new statistically-based methodology to organize and retrieve images of natural scenes by combining feature extraction, automatic clustering, automatic indexing and classification techniques. Our proposal belongs to the content-based image retrieval (CBIR) category. Our goal is to retrieve images from an image database by their content. The methodology combines randomly extracted points for feature extraction. The describing features are the mean, the standard deviation and the homogeneity (from the co-occurrence matrix) of a sub-image extracted from the three color channels (HSI). A K-means algorithm and a 1-NN classifier are used to build an indexed database. Three databases of images of natural scenes are used during the training and testing processes. One of the advantages of our proposal is that the images are not labeled manually for their retrieval. The performance of our framework is shown through several experimental results, including a comparison with several classifiers and comparison with related works, achieving up to 100% good recognition. Additionally, our proposal includes scene retrieval.     

融合HU不变矩和SIFT特征的商标检索

利用商标图像的形状特征,提出了一种融合图像全局特征和局部特征的商标检索算法。其中全局特征反应了图像的整体信息,这些信息可用来较快地建立候选图像库,而局部特征则可以更准确地与候选图像进行匹配。提取图像的HU不变矩进行初步检索,按相似度排序,在此结果集的基础上对候选图像通过提取SIFT特征进行精确匹配。实验结果表明,该方法既保持了SIFT特征的良好描述能力,又减少了精确匹配需要的计算次数,降低了复杂度。     

用分块图像特征进行商标图像检索

提出一种使用全局和局部图像特征检索商标图像的方法．首先确定图像的形状主方向，根据形状主方向对图像进行旋转；然后对旋转后的图像提取目标区域，对目标区域用四叉树分解的方法划分多级分块；最后抽取分块图像特征进行图像相似性度量．对图像库中2000幅商标图像实验表明，分块图像特征具有良好的旋转、平移、尺度和变形不变性，得到的检索结果能够很好地满足人的视觉感受．     

基于内容的图像检索算法研究

在基于内容的图像检索中,图像特征的提取和匹配是两个关键性环节.相对于传统的方法(采用图像的单一特征和相似性计算标准的方法),提出了提取多种图像特征,并对不同的特征采用不同的相似性计算标准方法进行图像检索,采用动态权值的方法对检索出的图像的给出最终排名,实验结果表明,该方法具有更好的适应性和鲁棒性.     

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.     

Retrieval of translated, rotated and scaled color textures

A new method for color texture retrieval using color and edge features is proposed in this study. The proposed method unifies color and edge features rather than simply analyzing only color characteristics. First, the distributions of color and local edge patterns are used to derive a similarity measure for a pair of textures. Then, a retrieval method based on the similarity measure is proposed to retrieve texture images from a database of color textures. Finally, the similarity measure is extended to retrieve texture regions from a database of natural images. Since the proposed feature distributions can resist variations in translation, rotation and scale, our method has the ability to retrieve texture images or regions that change in translation, rotation and/or scale. The effectiveness and practicability of the proposed method have been demonstrated by various experiments.     

K均值聚类分割的多特征图像检索方法

从图像数据库中快速、准确地检索出所需要的图像,具有广泛的应用前景。针对使用单一图像特征难以准确表达图像之间的差异问题,提出了一种利用颜色聚类分割和形状特征提取的图像检索算法。选择符合人眼视觉特征的HSV空间,分别重组最能描述图像颜色特征的H分量和形状特征的V分量;用K均值聚类算法对两个分量进行聚类分割,得到目标物体;提取目标物体的Hu不变矩和傅里叶描述子来描述形状特征;用欧式距离进行相似度测量并用于图像检索中。采用不同类型图像进行实验,结果表明该算法优于使用单一特征和一般分割方法的图像检索技术。     

一种基于灰度共生矩阵的云图检索方法

卫星云图检索可帮助气象预报人员快速定位历史相似天气．根据云图纹理特征区分度较大的特点提出一种采用纹理特征对卫星云图进行相似性检索的方法。针时找到一个普遍适用的纹理特征非常困难的问题．提出一种根据特征值的方差分布情况从大量备选特征中快速找出适合某类图像检索所需的纹理特征值的方法,并以灰度共生矩阵的特征值提取为例．对卫星云图进行相似性检索。检索流程为：首先对云图进行云地分离的预处理．然后从云图的灰度共生矩阵中提取有效的检索特征生成特征值．并与历史云图库对应的特征库进行相似距离计算．最后根据距离的排序顺序输出最终的检索结果。实验表明．该方法能有效地从历史云图库中检索出具有相似视觉特征的云图．说明该方法可以用于卫星云图的相似性检索。     

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.     

Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales. A cultural heritage image is one of the fine-grained images because each image has the same similarity in most cases. Using the classification technique, distinguishing cultural heritage architecture may be difficult. This study proposes a cultural heritage content retrieval method using adaptive deep learning for fine-grained image retrieval. The key contribution of this research was the creation of a retrieval model that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cultural heritage image. The goal of the proposed method is to perform a retrieval task for classes. Incremental learning for new classes was conducted to reduce the re-training process. In this step, the original class is not necessary for re-training which we call an adaptive deep learning technique. Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learning and image processing. We analyze the experimental results of incremental learning for fine-grained images with images of Thai archaeological site architecture from world heritage provinces in Thailand, which have a similar architecture. Using a fine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category. The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent. Adaptive deep learning for fine-grained image retrieval was used to retrieve cultural heritage content, and it outperformed state-of-the-art methods in fine-grained image retrieval.     

A human-oriented image retrieval system using interactive genetic algorithm

Content-based image retrieval has been actively studied in several fields. This provides more effective management and retrieval of images than the keyword-based approach. However, most of the conventional methods lack the capability to effectively incorporate human intuition and emotion into retrieving images. It is difficult to obtain satisfactory results when the user wants the image that cannot be explicitly described or can be requested only based on impression. In order to solve this problem and supplement the lack of the user's expression capability, we have developed an image retrieval system based on human preference and emotion by using an interactive genetic algorithm (IGA). This system extracts the feature from images by wavelet transform, and provides a user-friendly means to retrieve an image from a large database when the user cannot clearly define what the image must be. Therefore, this facilitates the search for the image not only with explicit queries, but also with implicit queries such as "cheerful impression," "gloomy impression," and so on. A thorough experiment with a 2000 image database shows the usefulness of the proposed system.     

Retrieval of images of man-made structures based on projective invariance

In this paper we propose a geometry-based image retrieval scheme that makes use of projectively invariant features. Cross-ratio (CR) is an invariant feature under projective transformations for collinear points. We compute the CRs of point sets in quadruplets and the CR histogram is used as the feature for retrieval purposes. Being a geometric feature, it allows us to retrieve similar images irrespective of view point and illumination changes. We can retrieve the same building even if the facade has undergone a fresh coat of paints! Color and textural features can also be included, if desired. Experimental results show a favorably very good retrieval accuracy when tested on an image database of size 4000. The method is very effective in retrieving images having man-made objects rich in polygonal structures like buildings, rail tracks, etc.     

基于组合欧拉向量与边缘方向直方图的图像检索方法

在图像数据库中,如何有效检索和查询图像是一个重要的研究内容.文中提出一种结合组合欧拉向量与边缘方向直方图( EOH)的图像检索方法.首先,从边缘图像中提取组合欧拉向量特征进行图像检索(EEXO算法),其次,为更好地区分不同形状但欧拉特征相近的图像,将EEXO算法与EOH算法相结合提出EEXOEOH图像检索算法.实验结果表明,EEXOEOH算法与其它4种算法相比,具有较好的检索效率.     

基于多语义特征融合的图像检索

多媒体技术的发展导致数字图像迅速增长,如何根据语义特征高效检索出满足用户要求的图像,已成为当前各行业迫切需要解决的问题。为此提出一种基于颜色、纹理和形状三种语义特征的图像检索方法,建立了颜色和纹理特征的语义描述,使用BP神经网络实现了低层视觉特征到高层语义特征的映射。选取Corel图像库作为测试图像库,实验通过与基于颜色语义特征的检索方法相比较,取得了良好的实验效果。     

Unsupervised feature selection applied to content-based retrieval of lung images

This paper describes a new hierarchical approach to content-based image retrieval called the "customized-queries" approach (CQA). Contrary to the single feature vector approach which tries to classify the query and retrieve similar images in one step, CQA uses multiple feature sets and a two-step approach to retrieval. The first step classifies the query according to the class labels of the images using the features that best discriminate the classes. The second step then retrieves the most similar images within the predicted class using the features customized to distinguish "subclasses" within that class. Needing to find the customized feature subset for each class led us to investigate feature selection for unsupervised learning. As a result, we developed a new algorithm called FSSEM (feature subset selection using expectation-maximization clustering). We applied our approach to a database of high resolution computed tomography lung images and show that CQA radically improves the retrieval precision over the single feature vector approach. To determine whether our CBIR system is helpful to physicians, we conducted an evaluation trial with eight radiologists. The results show that our system using CQA retrieval doubled the doctors' diagnostic accuracy.