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.     

Adaptive image segmentation for region-based object retrieval using generalized Hough transform

Finding an object inside a target image by querying multimedia data is desirable, but remains a challenge. The effectiveness of region-based representation for content-based image retrieval is extensively studied in the literature. One common weakness of region-based approaches is that perform detection using low level visual features within the region and the homogeneous image regions have little correspondence to the semantic objects. Thus, the retrieval results are often far from satisfactory. In addition, the performance is significantly affected by consistency in the segmented regions of the target object from the query and database images. Instead of solving these problems independently, this paper proposes region-based object retrieval using the generalized Hough transform (GHT) and adaptive image segmentation. The proposed approach has two phases. First, a learning phase identifies and stores stable parameters for segmenting each database image. In the retrieval phase, the adaptive image segmentation process is also performed to segment a query image into regions for retrieving visual objects inside database images through the GHT with a modified voting scheme to locate the target visual object under a certain affine transformation. The learned parameters make the segmentation results of query and database images more stable and consistent. Computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy, robustness, and execution speed.     

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.     

Graph matching for object recognition and recovery

A robust skeleton-based graph matching method for object recognition and recovery applications is presented. The object model uses both a skeleton model and contour segment models, for object recognition and recovery. The presented skeleton-based shape matching method uses a combination of both structural and statistical methods that are applied in a sequential manner, which largely reduce the matching space when compared with previous works. This also provides a good alternate means to alleviate difficulties encountered in segmentation problems. Experiments of object recovery using real biomedical image samples have shown satisfactory results.     

An effective image retrieval mechanism using family-based spatial consistency filtration with object region

How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of ``TREC Video Retrieval Evaluation 2005 (TRECVID2005)'. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.     

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.

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。     

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。     

Spatially aware feature selection and weighting for object retrieval

Many recent image retrieval methods are based on the “bag-of-words” (BoW) model with some additional spatial consistency checking. This paper proposes a more accurate similarity measurement that takes into account spatial layout of visual words in an offline manner. The similarity measurement is embedded in the standard pipeline of the BoW model, and improves two features of the model: i) latent visual words are added to a query based on spatial co-occurrence, to improve query recall; and ii) weights of reliable visual words are increased to improve the precision. The combination of these methods leads to a more accurate measurement of image similarity. This is similar in concept to the combination of query expansion and spatial verification, but does not require query time processing, which is too expensive to apply to full list of ranked results. Experimental results demonstrate the effectiveness of our proposed method on three public datasets.     

Context-sensitive queries for image retrieval in digital libraries

In this paper we show how to achieve a more effective Query By Example processing, by using active mechanisms of biological vision, such as saccadic eye movements and fixations. In particular, we discuss the way to generate two fixation sequences from a query image I _q and a test image I _t of the data set, respectively, and how to compare the two sequences in order to compute a similarity measure between the two images. Meanwhile, we show how the approach can be used to discover and represent the hidden semantic associations among images, in terms of categories, which in turn drive the query process.     

Hessian optimal design for image retrieval

Recently there has been a considerable interest in active learning from the perspective of optimal experimental design (OED). OED selects the most informative samples to minimize the covariance matrix of the parameters, so that the expected prediction error of the parameters, as well as the model output, can be minimized. Most of the existing OED methods are based on either linear regression or Laplacian regularized least squares (LapRLS) models. Although LapRLS has shown a better performance than linear regression, it suffers from the fact that the solution is biased towards a constant and the lack of extrapolating power. In this paper, we propose a novel active learning algorithm called Hessian optimal design (HOD). HOD is based on the second-order Hessian energy for semi-supervised regression which overcomes the drawbacks of Laplacian based methods. Specifically, HOD selects those samples which minimize the parameter covariance matrix of the Hessian regularized regression model. The experimental results on content-based image retrieval have demonstrated the effectiveness of our proposed approach.     

基于视觉一致性的图像检索

提出一种新的彩色图像分割方法,将图像分割成具有明显视觉一致性的区域,这种一致性能够模拟人观察图像时的视觉感受,例如图像中的一片区域具有相同的颜色、纹理。对这样的一致性区域建立特征描述符,如颜色编码、连通系数、面积比例,其中颜色编码是通过将像素在HSI颜色空间中量化得到,进而为整幅图像建立特征描述;然后将这种特征描述用于图像的检索。实验结果表明,这种方法不仅能够很好地模拟图像所带给人的视觉感受,而且对具有视觉一致性的图像检索效果也有很好的表现。     

Exploring statistical correlations for image retrieval

Bridging the cognitive gap in image retrieval has been an active research direction in recent years, of which a key challenge is to get enough training data to learn the mapping functions from low-level feature spaces to high-level semantics. In this paper, image regions are classified into two types: key regions representing the main semantic contents and environmental regions representing the contexts. We attempt to leverage the correlations between types of regions to improve the performance of image retrieval. A Context Expansion approach is explored to take advantages of such correlations by expanding the key regions of the queries using highly correlated environmental regions according to an image thesaurus. The thesaurus serves as both a mapping function between image low-level features and concepts and a store of the statistical correlations between different concepts. It is constructed through a data-driven approach which uses Web data (images, their surrounding textual annotations) as training data source to learn the region concepts and to explore the statistical correlations. Experimental results on a database of 10,000 general-purpose images show the effectiveness of our proposed approach in both improving search precision (i.e. filter irrelevant images) and recall (i.e. retrieval relevant images whose context may be varied). Several major factors which have impact on the performance of our approach are also studied.     

Multiple representations, similarity matching, and results fusion for content-based image retrieval

In this paper, we show how the use of multiple content representations and their fusion can improve the performance of content-based image retrieval systems. We consider the case of texture and propose a new algorithm for texture retrieval based on multiple representations and their results fusion. Texture content is modeled using two different models: the well-known autoregressive model and a perceptual model based on perceptual features such as coarseness and directionality. In the case of the perceptual model, two viewpoints are considered: perceptual features are computed based on the original images viewpoint and on the autocovariance function viewpoint (corresponding to original images). So we consider a total of three content representations. The similarity measure used is based on Gower's index of similarity. Simple results of the fusion models are used to merge search results returned by different representations. Experimentations and benchmarking carried out on the well-known Brodatz database show a drastic improvement in search effectiveness with the fused model without necessarily altering their efficiency in an important way.     

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.     

Combining intra-image and inter-class semantics for consumer image retrieval

Unconstrained consumer photos pose great challenge for content-based image retrieval. Unlike professional images or domain-specific images, consumer photos vary significantly. More often than not, the objects in the photos are ill-posed, occluded, and cluttered with poor lighting, focus and exposure. In this paper, we propose a cascading framework for combining intra-image and inter-class similarities in image retrieval, motivated from probabilistic Bayesian principles. Support vector machines are employed to learn local view-based semantics based on just-in-time fusion of color and texture features. A new detection-driven block-based segmentation algorithm is designed to extract semantic features from images. The detection-based indexes also serve as input for support vector learning of image classifiers to generate class-relative indexes. During image retrieval, both intra-image and inter-class similarities are combined to rank images. Experiments using query-by-example on 2400 genuine heterogeneous consumer photos with 16 semantic queries show that the combined matching approach is better than matching with single index. It also outperformed the method of combining color and texture features by 55% in average precision.     

Neighborhood preserving regression for image retrieval

In content-based image retrieval (CBIR), relevance feedback has been proven to be a powerful tool for bridging the gap between low level visual features and high level semantic concepts. Traditionally, relevance feedback driven CBIR is often considered as a supervised learning problem where the user provided feedbacks are used to learn a distance metric or classification function. However, CBIR is intrinsically a semi-supervised learning problem in which the testing samples (images in the database) are present during the learning process. Moreover, when there are no sufficient feedbacks, these methods may suffer from the overfitting problem. In this paper, we propose a novel neighborhood preserving regression algorithm which makes efficient use of both labeled and unlabeled images. By using the unlabeled images, the geometrical structure of the image space can be incorporated into the learning system through a regularizer. Specifically, from all the functions which minimize the empirical loss on the labeled images, we select the one which best preserves the local neighborhood structure of the image space. In this way, our method can obtain a regression function which respects both semantic and geometrical structures of the image database. We present experimental evidence suggesting that our algorithm is able to use unlabeled data effectively for image retrieval.