Retrieving Similar Shapes Effectively and Efficiently

In this paper, we address the following problem: given a large collection of shapes and a query shape, retrieve all shapes (from the shape database) that are similar to the query shape. A generalized centroid-radii model is used to model all forms of shapes — convex shapes, concave shapes and shapes with holes. Under the model, a shape is represented by a set of vectors, each obtained from the radii emanating from the centroid of a virtual concentric ring.The model can also facilitate 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. To speed up the retrieval of similar shapes, we also propose a multi-level R-tree index, called the Nested R-trees (NR-trees). Unlike traditional high-dimensional index structures that index a high-dimensional point as it is (with its full dimension), the NR-trees splits the dimensionality of the point into a set of lower dimensions that are indexed by levels of the NR-trees. We also proposed a quick filtering mechanism to further prune the search space.We implemented a shape retrieval system that employs the generalized centroid-radii model and the NR-trees with the filtering mechanism. Our experimental study shows the effectiveness of the proposed shape model, and the efficiency of the NR-trees. The results also show that the filtering mechanism can significantly reduce the retrieval time.     

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。     

Using extended feature objects for partial similarity retrieval

In this paper, we introduce the concept of extended feature objects for similarity retrieval. Conventional approaches for similarity search in databases map each object in the database to a point in some high-dimensional feature space and define similarity as some distance measure in this space. For many similarity search problems, this feature-based approach is not sufficient. When retrieving partially similar polygons, for example, the search cannot be restricted to edge sequences, since similar polygon sections may start and end anywhere on the edges of the polygons. In general, inherently continuous problems such as the partial similarity search cannot be solved by using point objects in feature space. In our solution, we therefore introduce extended feature objects consisting of an infinite set of feature points. For an efficient storage and retrieval of the extended feature objects, we determine the minimal bounding boxes of the feature objects in multidimensional space and store these boxes using a spatial access structure. In our concrete polygon problem, sets of polygon sections are mapped to 2D feature objects in high-dimensional space which are then approximated by minimal bounding boxes and stored in an R-tree. The selectivity of the index is improved by using an adaptive decomposition of very large feature objects and a dynamic joining of small feature objects. For the polygon problem, translation, rotation, and scaling invariance is achieved by using the Fourier-transformed curvature of the normalized polygon sections. In contrast to vertex-based algorithms, our algorithm guarantees that no false dismissals may occur and additionally provides fast search times for realistic database sizes. We evaluate our method using real polygon data of a supplier for the car manufacturing industry. Edited by R. Güting. Received October 7, 1996 / Accepted March 28, 1997     

综合颜色和纹理特征的图像检索算法

基于内容的图像检索是当前多媒体信息检索的热点之一。基于内容的图像检索技术是根据对图像内容(特征)的描述和提取,在图像库中找到具有指定内容(特征)的图像。本文对图像颜色特征和纹理特征的提取、相似性度量等基于内容的图像检索的关键技术进行了分析和研究,并在此基础上,提出了一个基于颜色特征和纹理特征的图像检索算法并验证了其有效性。该算法采用HSV颜色空间的直方图作为颜色特征向量,采用灰度共生矩阵的四个纹理特征:能量、熵、惯性矩和相关性构成纹理特征向量,采用欧氏距离进行相似性度量。实验结果表明,该算法实现的系统具有良好的图像检索功能。     

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。     

一种基于彩色边缘综合特征的图像检索算法

图像特征的提取与表达是基于内容的图像检索技术基础。边缘是重要的视觉感知信息,也是图像最基本的特征之一,其在图像分析和理解中有重要价值。文中以视觉重要的图像边缘轮廓为基础,提出一种基于彩色边缘综合特征的图像检索算法。该算法首先利用Canny检测算子提取出原始图像的彩色边缘轮廓。然后构造出能全面反映边缘轮廓内容的3种直方图,即加权颜色直方图、角度直方图和梯度方向直方图。最后综合利用上述3种彩色边缘直方图计算图像间的内容相似度,并进行彩色图像检索。仿真实验表明,该算法能够准确和高效地查找出用户所需内容的彩色图像,并且具有较好的查准率和查全率。     

Content-based image retrieval techniques for the analysis of dermatological lesions using particle swarm optimization technique

This method presents extraction of effective color and shape features for the analysis of dermatology images. We employ three phases of operation in order to perform efficient retrieval of images of skin lesions. Our proposed algorithm used color and shape feature vectors and the features are normalized using Min–Max normalization. Particle swarm optimization (PSO) technique for multi-class classification is used to converge the search space more efficiently. The results using receiver operating characteristic (ROC) curve proved that the proposed architecture is highly contributed to computer-aided diagnosis of skin lesions. Experiments on a set of 1450 images yielded a specificity of 98.22% and a sensitivity of 94%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works. We present explicit combinations of feature vectors corresponding to healthy and lesion skin.     

基于分形编码拓扑特性的图像检索

为了更有效、更准确地进行图像检索，提出了一种利用分形编码这项重要的拓扑特性来处理图像索引的新方法，即将图像经分形编码，首先得到每张图像的迭代函数，然后将其伴随图像存人数据库中，成为该图像的索引文件最后对数据库进行搜索时，则通过对此索引文件的比对来找出与查询图像相似的图像。反观使用其他方法建立的图像索引数据库，则无法证明其建立的索引文件具有上述特质。实验显示，图像经过分形编码所表现出的几何性质以及独特的有效性和鲁棒性，证明该方法是一个更有效率、准确度高的检索方法。     

High-dimensional similarity joins

Many emerging data mining applications require a similarity join between points in a high-dimensional domain. We present a new algorithm that utilizes a new index structure, called the ε tree, for fast spatial similarity joins on high-dimensional points. This index structure reduces the number of neighboring leaf nodes that are considered for the join test, as well as the traversal cost of finding appropriate branches in the internal nodes. The storage cost for internal nodes is independent of the number of dimensions. Hence, the proposed index structure scales to high-dimensional data. We analyze the cost of the join for the ε tree and the R-tree family, and show that the ε tree will perform better for high-dimensional joins. Empirical evaluation, using synthetic and real-life data sets, shows that similarity join using the ε tree is twice to an order of magnitude faster than the R⁺ tree, with the performance gap increasing with the number of dimensions. We also discuss how some of the ideas of the ε tree can be applied to the R-tree family. These biased R-trees perform better than the corresponding traditional R-trees for high-dimensional similarity joins, but do not match the performance of the ε tree     

一种基于颜色统计聚类的医学图像检索技术

基于颜色检索的基本思想是将图像间的距离归结为其颜色直方图间的相似性度量,从而图像检索也就转化为颜色直方图的匹配。目前基于颜色检索的算法主要集中在不同颜色空间进行全局颜色聚类或融合其他可视特征(纹理,颜色空间信息等)联合检索两个方向上。该文在具体的结肠镜图像检索系统研究中,根据医学图像的特点,提出一种在HSV空间的颜色统计聚类的检索方法,取得了良好的检索效果。     

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.     

高效聚类索引表图像检索

提出了一种基于主颜色、聚类索引表的彩色图像检索算法。应用MPEG-7视觉内容描述对彩色图像进行量化处理,选取图像的主颜色及其所占百分率作为颜色特征,根据主颜色组合建立聚类索引数据库。利用上述主颜色特征计算图像间的相似度,利用聚类索引表对图像进行聚类和快速检索。实验表明,该算法能够准确和高效地检索出用户所需的彩色图像,具有较快的检索速度。     

基于颜色空间分布特征的图像检索

目前,基于颜色特征的图像检索大多是以图像的颜色直方图作为颜色特征,这种图像检索方法有简单高效的优点,但丢失了颜色的空间分布信息,该文从CT图像重建的理论中得到启发,将对一幅图像从几个方向的投影图作为这幅图像的颜色特征分布。为进一步减少检索时运算的数据量,对图像做小波分解,然后对分解后图像的低频子带做Radon变换得到颜色空间分布的特征向量,并根据这个特征进行检索,实验表明,当检索图像中有明显的颜色目标时,该方法比传统的颜色直方图法更精确,颜色空间性更强,而且检索用时更短。     

Kernelized locality-sensitive hashing

Fast retrieval methods are critical for many large-scale and data-driven vision applications. Recent work has explored ways to embed high-dimensional features or complex distance functions into a low-dimensional Hamming space where items can be efficiently searched. However, existing methods do not apply for high-dimensional kernelized data when the underlying feature embedding for the kernel is unknown. We show how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm's sublinear time similarity search guarantees for a wide class of useful similarity functions. Since a number of successful image-based kernels have unknown or incomputable embeddings, this is especially valuable for image retrieval tasks. We validate our technique on several data sets, and show that it enables accurate and fast performance for several vision problems, including example-based object classification, local feature matching, and content-based retrieval.     

An Efficient Deep Learning-based Content-based Image Retrieval Framework

The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology. Image retrieval has become one of the vital tools in image processing applications. Content-Based Image Retrieval (CBIR) has been widely used in varied applications. But, the results produced by the usage of a single image feature are not satisfactory. So, multiple image features are used very often for attaining better results. But, fast and effective searching for relevant images from a database becomes a challenging task. In the previous existing system, the CBIR has used the combined feature extraction technique using color auto-correlogram, Rotation-Invariant Uniform Local Binary Patterns (RULBP) and local energy. However, the existing system does not provide significant results in terms of recall and precision. Also, the computational complexity is higher for the existing CBIR systems. In order to handle the above mentioned issues, the Gray Level Co-occurrence Matrix (GLCM) with Deep Learning based Enhanced Convolution Neural Network (DLECNN) is proposed in this work. The proposed system framework includes noise reduction using histogram equalization, feature extraction using GLCM, similarity matching computation using Hierarchal and Fuzzy c- Means (HFCM) algorithm and the image retrieval using DLECNN algorithm. The histogram equalization has been used for computing the image enhancement. This enhanced image has a uniform histogram. Then, the GLCM method has been used to extract the features such as shape, texture, colour, annotations and keywords. The HFCM similarity measure is used for computing the query image vector's similarity index with every database images. For enhancing the performance of this image retrieval approach, the DLECNN algorithm is proposed to retrieve more accurate features of the image. The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy, precision, recall, f-measure and lesser complexity. From the experimental results, it is clearly observed that the proposed system provides efficient image retrieval for the given query image.     

Adaptive content-based music retrieval system

This paper presents a tunable content-based music retrieval (CBMR) system suitable the for retrieval of music audio clips. The audio clips are represented as extracted feature vectors. The CBMR system is expert-tunable by altering the feature space. The feature space is tuned according to the expert-specified similarity criteria expressed in terms of clusters of similar audio clips. The main goal of tuning the feature space is to improve retrieval performance, since some features may have more impact on perceived similarity than others. The tuning process utilizes our genetic algorithm. The R-tree index for efficient retrieval of audio clips is based on the clustering of feature vectors. For each cluster a minimal bounding rectangle (MBR) is formed, thus providing objects for indexing. Inserting new nodes into the R-tree is efficiently performed because of the chosen Quadratic Split algorithm. Our CBMR system implements the point query and the n-nearest neighbors query with the O(logn) time complexity. Different objective functions based on cluster similarity and dissimilarity measures are used for the genetic algorithm. We have found that all of them have similar impact on the retrieval performance in terms of precision and recall. The paper includes experimental results in measuring retrieval performance, reporting significant improvement over the untuned feature space.     

SIMPLIcity: semantics-sensitive integrated matching for picturelibraries

We present here SIMPLIcity (semantics-sensitive integrated matching for picture libraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. An image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. The application of SIMPLIcity to several databases has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations     

基于R树的图像检索方法

高维索引技术作为高维空间数据的快速查询手段,对使用高维数据的基于内容图像检索有着广泛的应用。本文提出以Guttm an提出的R树结构建立存储图像的特征值的高维索引结构来提高图像检索效率。首先对R树的结构进行介绍,然后通过对比相同情况下使用线性查询和R树查询各自的查询次数和查询时间分析R树查询的优势。实验结果表明,利用R树结构可以减少图像检索的查询次数和查询时间,明显地提高图像检索的效率。