Visual object retrieval via block-based visual-pattern matching

This paper presents an object-based image retrieval using a method based on visual-pattern matching. A visual pattern is obtained by detecting the line edge from a square block using the moment-preserving edge detector. It is desirable and yet remains as a challenge for querying multimedia data by finding an object inside a target image. Given an object model, an added difficulty is that the object might be translated, rotated, and scaled inside a target image. Object segmentation and recognition is the primary step of computer vision for applying to image retrieval of higher-level image analysis. However, automatic segmentation and recognition of objects via object models is a difficult task without a priori knowledge about the shape of objects. Instead of segmentation and detailed object representation, the objective of this research is to develop and apply computer vision methods that explore the structure of an image object by visual-pattern detection to retrieve images from a database. A voting scheme based on generalized Hough transform is proposed to provide object search method, which is invariant to the translation, rotation, scaling of image data, and hence, invariant to orientation and position. Computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy and robustness.     

Visual guided navigation for image retrieval

In this work, we are interested in technologies that will allow users to actively browse and navigate large image databases and to retrieve images through interactive fast browsing and navigation. The development of a browsing/navigation-based image retrieval system has at least two challenges. The first is that the system's graphical user interface (GUI) should intuitively reflect the distribution of the images in the database in order to provide the users with a mental picture of the database content and a sense of orientation during the course of browsing/navigation. The second is that it has to be fast and responsive, and be able to respond to users actions at an interactive speed in order to engage the users. We have developed a method that attempts to address these challenges of a browsing/navigation based image retrieval systems. The unique feature of the method is that we take an integrated approach to the design of the browsing/navigation GUI and the indexing and organization of the images in the database. The GUI is tightly coupled with the algorithms that run in the background. The visual cues of the GUI are logically linked with various parts of the repository (image clusters of various particular visual themes) thus providing intuitive correspondences between the GUI and the database contents. In the backend, the images are organized into a binary tree data structure using a sequential maximal information coding algorithm and each image is indexed by an n-bit binary index thus making response to users’ action very fast. We present experimental results to demonstrate the usefulness of our method both as a pre-filtering tool and for developing browsing/navigation systems for fast image retrieval from large image databases.     

个性化图像检索中用户兴趣模型的构建方法

针对个性化图像检索的语义鸿沟问题,提出了一种新的用户兴趣模型的构建方法。将用户兴趣模型分为长期兴趣和短期兴趣：用户的短期兴趣由图像的低层特征映射得到;用户的长期兴趣经过推理机推理,将短期兴趣映射为高层语义得到,从而弥补语义鸿沟。实验结果表明,经过用户兴趣模型过滤的图像检索结果符合用户的个性化要求,相比已有方法在查准率和查全率上取得了明显的改善。     

Ranking consistency for image matching and object retrieval

The goal of object retrieval is to rank a set of images by the similarity of their contents to those of a query image. However, it is difficult to measure image content similarity due to visual changes caused by varying viewpoint and environment. In this paper, we propose a simple, efficient method to more effectively measure content similarity from image measurements. Our method is based on the ranking information available from existing retrieval systems. We observe that images within the set which, when used as queries, yield similar ranking lists are likely to be relevant to each other and vice versa. In our method, ranking consistency is used as a verification method to efficiently refine an existing ranking list, in much the same fashion that spatial verification is employed. The efficiency of our method is achieved by a list-wise min-Hash scheme, which allows rapid calculation of an approximate similarity ranking. Experimental results demonstrate the effectiveness of the proposed framework and its applications.     

一种综合区域匹配的图像检索改进算法

为了能更准确地表达图像信息,提高系统检索性能,提出了一种基于综合区域匹配（IRM）的改进算法。先采用阈值和模糊C-均值相结合的方法分割图像,再采用改进了综合区域距离和重要性因子算法的IRM方法进行图像匹配,并根据图像目标和背景的面积比关系提出“有效距离”概念。实验结果表明,相对于经典算法,改进后算法的平均查准率增加了4.58%。该方法能广泛应用于图像检索系统,具有较大适用性。     

FPGA-based minutia matching for biometric fingerprint image database retrieval

In this paper, a parallel-matching processor architecture with early jump-out (EJO) control is proposed to carry out high-speed biometric fingerprint database retrieval. The processor performs the fingerprint retrieval by using minutia point matching. An EJO method is applied to the proposed architecture to speed up the large database retrieval. The processor is implemented on a Xilinx Virtex-E, and occupies 6,825 slices and runs at up to 65 MHz. The software/hardware co-simulation benchmark with a database of 10,000 fingerprints verifies that the matching speed can achieve the rate of up to 1.22 million fingerprints per second. EJO results in about a 22% gain in computing efficiency.

基于层级匹配的维吾尔文关键词文档图像检索

为实现基于关键词的维吾尔文文档图像检索,提出一种基于由粗到细层级匹配的关键词文档图像检索方法。使用改进的投影切分法将经过预处理的文档图像切分成单词图像库,使用模板匹配对关键词进行粗匹配;在粗匹配的基础上,提取单词图像的方向梯度直方图(HOG)特征向量;通过支持向量机(SVM)分类器学习特征向量,实现关键词图像检索。在包含108张文档图像的数据库中进行实验,实验结果表明,检索准确率平均值为91.14%,召回率平均值为79.31%,该方法能有效实现基于关键词的维吾尔文文档图像检索。     

A new matching strategy for content based image retrieval system

Adopting effective model to access the desired images is essential nowadays with the presence of a huge amount of digital images. The present paper introduces an accurate and rapid model for content based image retrieval process depending on a new matching strategy. The proposed model is composed of four major phases namely: features extraction, dimensionality reduction, ANN classifier and matching strategy. As for the feature extraction phase, it extracts a color and texture features, respectively, called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). However, integrating multiple features can overcome the problems of single feature, but the system works slowly mainly because of the high dimensionality of the feature space. Therefore, the dimensionality reduction technique selects the effective features that jointly have the largest dependency on the target class and minimal redundancy among themselves. Consequently, these features reduce the calculation work and the computation time in the retrieval process. The artificial neural network (ANN) in our proposed model serves as a classifier so that the selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image. In addition, the proposed model presents an effective feature matching strategy that depends on the idea of the minimum area between two vectors to compute the similarity value between a query image and the images in the determined class. Finally, the results presented in this paper demonstrate that the proposed model provides accurate retrieval results and achieve improvement in performance with significantly less computation time compared with other models.     

Visual word spatial arrangement for image retrieval and classification

We present word spatial arrangement (WSA), an approach to represent the spatial arrangement of visual words under the bag-of-visual-words model. It lies in a simple idea which encodes the relative position of visual words by splitting the image space into quadrants using each detected point as origin. WSA generates compact feature vectors and is flexible for being used for image retrieval and classification, for working with hard or soft assignment, requiring no pre/post processing for spatial verification. Experiments in the retrieval scenario show the superiority of WSA in relation to Spatial Pyramids. Experiments in the classification scenario show a reasonable compromise between those methods, with Spatial Pyramids generating larger feature vectors, while WSA provides adequate performance with much more compact features. As WSA encodes only the spatial information of visual words and not their frequency of occurrence, the results indicate the importance of such information for visual categorization.     

Improving video event retrieval by user feedback

Multimedia Tools and Applications - In content based video retrieval videos are often indexed with semantic labels (concepts) using pre-trained classifiers. These pre-trained classifiers (concept...     

FReBIR: An image retrieval system based on fuzzy region matching

In this paper, we present a method of image indexing and retrieval which takes into account the relative positions of the regions within the image. Indexing is based on a segmentation of the image into fuzzy regions; we propose an algorithm which produces a fuzzy segmentation. The image retrieval is based on inexact graph matching, taking into account both the similarity between regions and the spatial relation between them. We propose, on one hand a solution to reduce the combinatorial complexity of the graph matching, and on the other hand, a measure of similarity between graphs allowing the result images ranking. A relevance feedback process based on region classifiers allows then a good generalization to a large variety of the regions. The method is adapted to partial queries, aiming for example at retrieving images containing a specific type of object. Applications may be of two types, firstly an on-line search from a partial query, with a relevance feedback aiming at interactively leading the search, and secondly an off-line learning of categories from a set of examples of the object. The name of the system is FReBIR for Fuzzy Region-Based Image Retrieval.     

A novel framework for retrieval of image using weighted edge matching algorithm

Multimedia Tools and Applications - With enormous development in diverse kinds of images through electronic communication networks, it becomes a demanding chore to retrieve the efficient result...     

Inexact graph matching based on kernels for object retrieval in image databases

In the framework of online object retrieval with learning, we address the problem of graph matching using kernel functions. An image is represented by a graph of regions where the edges represent the spatial relationships. Kernels on graphs are built from kernel on walks in the graph. This paper firstly proposes new kernels on graphs and on walks, which are very efficient for graphs of regions. Secondly we propose fast solutions for exact or approximate computation of these kernels. Thirdly we show results for the retrieval of images containing a specific object with the help of very few examples and counter-examples in the framework of an active retrieval scheme.     

基于高亮模型匹配的SAR建筑物高度反演方法

针对高分辨率SAR图像中的建筑物高度提取问题,提出了一种基于高亮模型匹配的建筑物高度反演方法。通过对建筑物的成像特征进行分析构建出高亮特征模型,建立模型与SAR图像之间的匹配度函数,运用多种群遗传算法对匹配度函数进行优化搜索出最优的高度参数。基于模拟和实测SAR图像的实验结果表明该算法可以用于SAR图像建筑物高度反演,并具有较高的反演精度。     

改进SURF特征的维吾尔文复杂文档图像匹配检索

针对图像局部特征的词袋模型（Bag-of-Word,BOW）检索研究中聚类中心的不确定性和计算复杂性问题,提出一种由不同种类的距离进行相似程度测量的检索和由匹配点数来检索的方法。这种方法首先需要改进文档图像的SURF特征,有效降低特征提取复杂度;其次,对FAST+SURF特征实现FLANN双向匹配与KD-Tree+BBF匹配,在不同变换条件下验证特征鲁棒性;最后,基于这两种检索方法对已收集整理好的各类维吾尔文文档图像数据库进行检索。实验结果表明:基于距离的相似性度量复杂度次于基于匹配数目的检索,而且两种检索策略都能满足快速、精确查找需求。     

OM-based video shot retrieval by one-to-one matching

This paper proposes a new approach for shot-based retrieval by optimal matching (OM), which provides an effective mechanism for the similarity measure and ranking of shots by one-to-one matching. In the proposed approach, a weighted bipartite graph is constructed to model the color similarity between two shots. Then OM based on Kuhn–Munkres algorithm is employed to compute the maximum weight of a constructed bipartite graph as the shot similarity value by one-to-one matching among frames. To improve the speed efficiency of OM, two improved algorithms are also proposed: bipartite graph construction based on subshots and bipartite graph construction based on the same number of keyframes. Besides color similarity, motion feature is also employed for shot similarity measure. A motion histogram is constructed for each shot, the motion similarity between two shots is then measured by the intersection of their motion histograms. Finally, the shot similarity is based on the linear combination of color and motion similarity. Experimental results indicate that the proposed approach achieves better performance than other methods in terms of ranking and retrieval capability.

基于加权特征空间信息视觉词典的图像检索模型

针对传统的视觉词袋模型中视觉词典对底层特征量化时容易引入量化误差,以及视觉单词的适用性不足等问题,提出了基于加权特征空间信息视觉词典的图像检索模型。从产生视觉词典的常用聚类算法入手,分析和探讨了聚类算法的特点,考虑聚类过程中特征空间的特征分布统计信息,通过实验对不同的加权方式进行对比,得出效果较好的均值加权方案,据此对视觉单词的重要程度加权,提高视觉词典的描述能力。对比实验表明,在ImageNet图像数据集上,相对于同源视觉词典,非同源视觉词典对视觉空间的划分影响较小,且基于加权特征空间信息视觉词典在大数据集上更加有效。