Image retrieval based on regions of interest

Query-by-example is the most popular query model in recent content-based image retrieval (CBIR) systems. A typical query image includes relevant objects (e.g., Eiffel Tower), but also irrelevant image areas (including background). The irrelevant areas limit the effectiveness of existing CBIR systems. To overcome this limitation, the system must be able to determine similarity based on relevant regions alone. We call this class of queries region-of-interest (ROI) queries and propose a technique for processing them in a sampling-based matching framework. A new similarity model is presented and an indexing technique for this new environment is proposed. Our experimental results confirm that traditional approaches, such as Local Color Histogram and Correlogram, suffer from the involvement of irrelevant regions. Our method can handle ROI queries and provide significantly better performance. We also assessed the performance of the proposed indexing technique. The results clearly show that our retrieval procedure is effective for large image data sets.     

Content-based image retrieval using growing hierarchical self-organizing quadtree map

In this paper, a growing hierarchical self-organizing quadtree map (GHSOQM) is proposed and used for a content-based image retrieval (CBIR) system. The incorporation of GHSOQM in a CBIR system organizes images in a hierarchical structure. The retrieval time by GHSOQM is less than that by using direct image comparison using a flat structure. Furthermore, the ability of incremental learning enables GHSOQM to be a prospective neural-network-based approach for CBIR systems. We also propose feature matrices, image distance and relevance feedback for region-based images in the GHSOQM-based CBIR system. Experimental results strongly demonstrate the effectiveness of the proposed system.     

一种面向大规模图像库的降维索引新方法

针对图像的72维HSV颜色特征，提出了一种新的降维方法。该方法在降维的过程中充分保留了图像颜色的本征特性。在降维的基础上，建立了一个新的索引机制，并以此加速大规模图像库的基于内容检索的进程。实验证明，该方法是行之有效的。     

Incremental indexing with binary feature based Tversky index using black hole entropic fuzzy clustering in cloud computing

Due to the large volume of computational and storage requirements of content based image retrieval (CBIR), outsourcing image to cloud providers become an attractive research. Even though, the cloud service provides efficient indexing of the condensed images, it remains a major issue in the process of incremental indexing. Hence, an effective incremental indexing mechanism named Black Hole Entropic Fuzzy Clustering +Deep stacked incremental indexing (BHEFC+deep stacked incremental indexing) is proposed in this paper to perform incremental indexing through the retrieval of images. The images are encrypted and stored in cloud server for ensuring the security of image retrieval process. The trained images are clustered using the clustering mechanism BHEFC based on Tversky index. With the incremental indexing process, the new training images are encrypted and are converted into the decimal form such that the weight is computed using deep stacked auto-encoder that enable to update the centroid with new score values. The experimental evaluations on benchmark datasets shows that the proposed BHEFC+deep stacked incremental indexing model achieves better results compared to the existing methods by obtaining maximum accuracy of 96.728%, maximum F-measure of 83.598%, maximum precision of 84.447%, and maximum recall of 94.817%, respectively.

     

Expert system for color image retrieval

Recently, as Web and various databases contain a large number of images, content-based image retrieval (CBIR) applications are greatly needed. This paper proposes a new image retrieval system using color-spatial information from those applications.First, this paper suggests two kinds of indexing keys to prune away irrelevant images to a given query image: major colors' set (MCS) signature related with color information and distribution block signature (DBS) related with spatial information. After successively applying these filters to a large database, we get only small amount of high potential candidates that are somewhat similar to a query image. Then we make use of the quad modeling (QM) method to set the initial weights of two-dimensional cell in a query image according to each major color. Finally, we retrieve more similar images from the database by comparing a query image with candidate images through a similarity measuring function associated with the weights. In that procedure, we use a new relevance feedback mechanism. This feedback enhances the retrieval effectiveness by dynamically modulating the weights of color-spatial information. Experiments show that the proposed system is not only efficient but also effective.     

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.     

基于索引和相关反馈的图像检索研究

在基于图像内容的图像检索(CBIR)系统中,搜索引擎检索图像类似于按照相似标准来查询图像,它应该有足够快的速度并且有较高的检索准确率.索引用来提高系统响应,而相关反馈用于帮助提高检索准确率.在本文中,主要说明基于人感知的相似性度量,以及讨论综合相关反馈的索引方案.该索引方案通过分析特征熵而得出的主从键,而相关反馈是根据Mann-Whitnev检验而提出的,该检验通常用来识别来自同一搜索集中相关图像和不相关图像之间不同特征,并利用不同特征的特点提高检索性能.相关反馈方案针对两不同相似标准来执行,检验判定了这个方法的有效性.最后,把索引机制和相关反馈机制结合起来建立搜索引擎.     

Robust image retrieval with hidden classes

For the purpose of content-based image retrieval (CBIR), image classification is important to help improve the retrieval accuracy and speed of the retrieval process. However, the CBIR systems that employ image classification suffer from the problem of hidden classes. The queries associated with hidden classes cannot be accurately answered using a traditional CBIR system. To address this problem, a robust CBIR scheme is proposed that incorporates a novel query detection technique and a self-adaptive retrieval strategy. A number of experiments carried out on the two popular image datasets demonstrate the effectiveness of the proposed scheme.     

基于聚类的图像检索

如何构建有效的组织和索引、提高图像检索速度是基于内容的图像检索所需解决的关键问题之一。论文采用了一种基于改进的模糊C均值算法的聚类索引。实验表明:该方法应用于图像检索,在准确性和实时性方面均能达到较好的效果,并优于已有的模糊C均值聚类算法。另外,系统实现了基于多特征结合的方法进行检索,并利用基于相关反馈的权重调整方法进一步提高检索性能,使检索结果更加符合用户的视觉效果。     

基于内容的图像检索的发展最新趋势

基于内容的图像检索目前主要集中于底层特征的相似度匹配的研究,文中阐述了基于内容的图像检索发展的最新趋势:基于语义内容的图像检索和语义的描述方法。文章首先提出了语义层次化的基于内容检索的系统框架,然后介绍了图像高层语义的处理方法,最后展望了基于MPEG-7的统一规范的图像语义的描述方法。     

A novel evolutionary approach for optimizing content-based image indexing algorithms.

Optimization of content-based image indexing and retrieval (CBIR) algorithms is a complicated and time-consuming task since each time a parameter of the indexing algorithm is changed, all images in the database should be indexed again. In this paper, a novel evolutionary method called evolutionary group algorithm (EGA) is proposed for complicated time-consuming optimization problems such as finding optimal parameters of content-based image indexing algorithms. In the new evolutionary algorithm, the image database is partitioned into several smaller subsets, and each subset is used by an updating process as training patterns for each chromosome during evolution. This is in contrast to genetic algorithms that use the whole database as training patterns for evolution. Additionally, for each chromosome, a parameter called age is defined that implies the progress of the updating process. Similarly, the genes of the proposed chromosomes are divided into two categories: evolutionary genes that participate to evolution and history genes that save previous states of the updating process. Furthermore, a new fitness function is defined which evaluates the fitness of the chromosomes of the current population with different ages in each generation. We used EGA to optimize the quantization thresholds of the wavelet-correlogram algorithm for CBIR. The optimal quantization thresholds computed by EGA improved significantly all the evaluation measures including average precision, average weighted precision, average recall, and average rank for the wavelet-correlogram method.     

Developing an object‐oriented framework for content‐based image retrieval

Content‐based image retrieval (CBIR) is a process of retrieving images from an image database by exploiting the content of the images (typically the querying of an image). CBIR avoids many problems associated with traditional ways of retrieving images by keywords. Thus, a growing interest in the area of CBIR has been established in recent years. In this paper, a novel object‐oriented framework (CBIRFrame) is built for CBIR applications development. We discuss the motivations for CBIRFrame before discussing its design in detail. Two applications of CBIRFrame are also briefly discussed to show the effectiveness of applying CBIRFrame to real applications. Finally, we outline the possible uses of the design of CBIRFrame for other types of domains, such as content‐based retrieval of video clips. Copyright © 2003 John Wiley & Sons, Ltd.     

Expert system design using wavelet and color vocabulary trees for image retrieval

A new image indexing and retrieval system for content based image retrieval (CBIR) is proposed in this paper. The characteristics (vector points) of image are computed using color (color histogram) and SOT (spatial orientation tree). The SOT defines the spatial parent-child relationship among wavelet coefficients in multi-resolution wavelet sub-bands. First the image is divided into sub-blocks and then constructed the SOT for each low pass wavelet coefficient is considered as a vector point of that particular image. Similarly the color histogram features are collected from the each sub-block. The vector points of each image are indexed using vocabulary tree. The retrieval results of the proposed method are tested on different image databases, i.e., natural image database consists of Corel 1000 (DB1), Brodatz texture image database (DB2) and MIT VisTex database (DB3). The results after being investigated show a significant improvement in terms of average precision, average recall and average retrieval rate on DB1 database and average retrieval rate on texture databases (DB2 and DB3) as compared with most of existing techniques on respective databases.     

基于内容的图象检索技术

随着数字图象的日益增多,基于内容的图象检索已成为图象使用者和管理者迫切需要解决的问题,近年来,各国研究者纷纷加入该领域的研究.为了使人们对该领域现状有个概略了解,以推动该领域研究进一步开展,首先概括介绍了基于内容图象检索的产生、发展及其关键技术;然后介绍了特征提取(包括低层特征和语义特征)及其相似性计算、相关反馈等的原理及算法;最后指出了基于内容的图象检索技术与计算机视觉技术的区别所在,并对目前存在的问题和应着重的研究内容以及发展方向进行了分析.     

Effective CBIR based on hybrid image features and multilevel approach

Multimedia Tools and Applications - Content based image retrieval (CBIR) process can retrieve images by matching its feature set values. The proposed novel CBIR methodology called Effective CBIR...     

HIRBIR: A hierarchical approach to region-based image retrieval

This paper proposes a hierarchical approach to region-based image retrieval (HIRBIR) based on wavelet transform whose decomposition property is similar to human visual processing. First, automated image segmentation is performed fast in the low-low (LL) frequency subband of the wavelet domain that shows the desirable low image resolution. In the proposed system, boundaries between segmented regions are deleted to improve the robustness of region-based image retrieval against segmentation-related uncertainty. Second, a region feature vector is hierarchically represented by information in all wavelet subbands, and each feature component of a feature vector is a unified color–texture feature. Such a feature vector captures well the distinctive features (e.g., semantic texture) inside one region. Finally, employing a hierarchical feature vector, the weighted distance function for region matching is tuned meaningfully and easily, and a progressive stepwise indexing mechanism with relevance feedback is performed naturally and effectively in our system. Through experimental results and comparison with other methods, the proposed HIRBIR shows a good tradeoff between retrieval effectiveness and efficiency as well as easy implementation for region-based image retrieval.     

A rotation- and flip-invariant algorithm for representing spatial continuity information of geographic images in content-based image retrieval

This research proposes a rotation- and flip-invariant algorithm for representing spatial continuity information in high-resolution geographic images for content based image retrieval (CBIR). Starting with variogram concept, the new visual property representation, in the form of a numeric index vector, consists of a set of semi-variances at selected lags and directions, based on three well-justified principles: (1) capture the basic shape of sample variogram, (2) represent the spatial continuity anisotropy, and (3) make the representation rotation- and flip-invariant. The algorithm goes through two tests. The first test confirms that it can indeed align the image representations based on spatial continuity information of objects within images by re-ordering the semi-variances accordingly. In the second test, the algorithm is applied to retrieve seven types of typical geographic entities from an Erie County orthophoto database. The retrieval results demonstrate the effectiveness of the new algorithm in CBIR, as assessed by retrieval precision.     

Embedding neural networks for semantic association in content based image retrieval

Content based image retrieval (CBIR) systems provide potential solution of retrieving semantically similar images from large image repositories against any query image. The research community are competing for more effective ways of content based image retrieval, so they can be used in serving time critical applications in scientific and industrial domains. In this paper a Neural Network based architecture for content based image retrieval is presented. To enhance the capabilities of proposed work, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis. For this wavelet packets and Eigen values of Gabor filters are used for image representation purposes. To ensure semantically correct image retrieval, a partial supervised learning scheme is introduced which is based on K-nearest neighbors of a query image, and ensures the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better then all of the comparative systems.

     

VISTO: A new CBIR system for vector images

In this paper, we present the main features of VISTO (Vector Image Search TOol), a new content-based image retrieval (CBIR) system for vector images. Though unsuitable for photo-realistic imagery, vector graphics are continually becoming more advanced and diffused. Vector images are fully scalable, resolution independent, not restricted to rectangular shape, allowing layering and editable/searchable text. Notwithstanding this increasing interest, the research area concerning CBIR systems for vectorial images is quite new, and our research on a vector based CBIR system actually derives from a precise request of vector based application experts that did not find appropriate solutions to their retrieval problems in customary shape-based CBIR system. To the best of our knowledge, VISTO is the first CBIR system for vector images proposed in the literature, and it supports the retrieval of images in SVG (scalable vector graphics) format.