Boosting image object retrieval and indexing by automatically discovered pseudo-objects

State-of-the-art object retrieval systems are mostly based on the bag-of-visual-words representation which encodes local appearance information of an image in a feature vector. An image object search is performed by comparing query object’s feature vector with those for database images. However, a database image vector generally carries mixed information of the entire image which may contain multiple objects and background. Search quality is degraded by such noisy (or diluted) feature vectors. To tackle this problem, we propose a novel representation, pseudo-objects – a subset of proximate feature points with its own feature vector to represent a local area, to approximate candidate objects in database images. In this paper, we investigate effective methods (e.g., grid, G-means, and GMM–BIC) to estimate pseudo-objects. Additionally, we also confirm that the pseudo-objects can significantly benefit inverted-file indexing both in accuracy and efficiency. Experimenting over two consumer photo benchmarks, we demonstrate that the proposed method significantly outperforms other state-of-the-art object retrieval and indexing algorithms.     

基于兴趣点局部分布特征的图像检索方法

提出了一种基于兴趣点颜色和空间分布特征的图像检索方法。该方法把图像内容看作为由若干兴趣点组成的集合,首先利用小波系数的空间方向树特性来检测兴趣点,然后利用基于兴趣点的环形颜色直方图和空间离散度来描述图像的特征,最后用加权特征距离来估计图像内容的相似度。同时,通过利用环形颜色直方图和空间离散度作为图像特征保证了该算法能够对图像的尺度变化、旋转变化和平移变化具有很好的抑制能力。在含有1000幅图像的数据库上所做的一系列实验表明,该算法与其它基于兴趣点的方法相比,能够更准确和高效地查找出用户所需的图像,明显地提高了检索精度。     

MRI and CT image indexing and retrieval using local mesh peak valley edge patterns

In this paper, a new pattern based feature, local mesh peak valley edge pattern (LMePVEP) is proposed for biomedical image indexing and retrieval. The standard LBP extracts the gray scale relationship between the center pixel and its surrounding neighbors in an image. Whereas the proposed method extracts the gray scale relationship among the neighbors for a given center pixel in an image. The relations among the neighbors are peak/valley edges which are obtained by performing the first-order derivative. The performance of the proposed method (LMePVEP) is tested by conducting two experiments on two benchmark biomedical databases. Further, it is mentioned that the databases used for experiments are OASIS−MRI database which is the magnetic resonance imaging (MRI) database and VIA/I–ELCAP-CT database which includes region of interest computer tomography (CT) images. The results after being investigated show a significant improvement in terms average retrieval precision (ARP) and average retrieval rate (ARR) as compared to LBP and LBP variant features.     

A new way for multidimensional medical data management: volume of interest (VOI)-based retrieval of medical images with visual and functional features.

The advances in digital medical imaging and storage in integrated databases are resulting in growing demands for efficient image retrieval and management. Content-based image retrieval (CBIR) refers to the retrieval of images from a database, using the visual features derived from the information in the image, and has become an attractive approach to managing large medical image archives. In conventional CBIR systems for medical images, images are often segmented into regions which are used to derive two-dimensional visual features for region-based queries. Although such approach has the advantage of including only relevant regions in the formulation of a query, medical images that are inherently multidimensional can potentially benefit from the multidimensional feature extraction which could open up new opportunities in visual feature extraction and retrieval. In this study, we present a volume of interest (VOI) based content-based retrieval of four-dimensional (three spatial and one temporal) dynamic PET images. By segmenting the images into VOIs consisting of functionally similar voxels (e.g., a tumor structure), multidimensional visual and functional features were extracted and used as region-based query features. A prototype VOI-based functional image retrieval system (VOI-FIRS) has been designed to demonstrate the proposed multidimensional feature extraction and retrieval. Experimental results show that the proposed system allows for the retrieval of related images that constitute similar visual and functional VOI features, and can find potential applications in medical data management, such as to aid in education, diagnosis, and statistical analysis.     

Efficient Content-Based Image Retrieval Methods Using Color and Texture

In this paper, we propose efficient content-based image retrieval methods using the automatic extraction of the low-level visual features as image content. Two new feature extraction methods are presented. The first one is an advanced color feature extraction derived from the modification of Stricker's method. The second one is a texture feature extraction using some DCT coefficients which represent some dominant directions and gray level variations of the image. In the experiment with an image database of 200 natural images, the proposed methods show higher performance than other methods. They can be combined into an efficient hierarchical retrieval method.     

基于灰度和边界方向直方图的医学图像检索

本文研究了采用分级检索的机制,综合利用灰度及形状特征进行基于内容的医学图像检索的方法,该方法克服了灰度直方图不能充分表示空间分布信息的不足。利用边界方向直方图描述形状特征,避开了对图像进行精确分割这一医学图像处理中的难点问题。对CT图像数据库进行的检索实验,验证了该方法具有良好的检索性能。     

Research on image feature extraction and retrieval algorithms based on convolutional neural network

With the rapid development of mobile Internet and digital technology, people are more and more keen to share pictures on social networks, and online pictures have exploded. How to retrieve similar images from large-scale images has always been a hot issue in the field of image retrieval, and the selection of image features largely affects the performance of image retrieval. The Convolutional Neural Networks (CNN), which contains more hidden layers, has more complex network structure and stronger ability of feature learning and expression compared with traditional feature extraction methods. By analyzing the disadvantage that global CNN features cannot effectively describe local details when they act on image retrieval tasks, a strategy of aggregating low-level CNN feature maps to generate local features is proposed. The high-level features of CNN model pay more attention to semantic information, but the low-level features pay more attention to local details. Using the increasingly abstract characteristics of CNN model from low to high. This paper presents a probabilistic semantic retrieval algorithm, proposes a probabilistic semantic hash retrieval method based on CNN, and designs a new end-to-end supervised learning framework, which can simultaneously learn semantic features and hash features to achieve fast image retrieval. Using convolution network, the error rate is reduced to 14.41% in this test set. In three open image libraries, namely Oxford, Holidays and ImageNet, the performance of traditional SIFT-based retrieval algorithms and other CNN-based image retrieval algorithms in tasks are compared and analyzed. The experimental results show that the proposed algorithm is superior to other contrast algorithms in terms of comprehensive retrieval effect and retrieval time.     

Query Expansion by Text and Image Features in Image Retrieval

We present a two-pass image retrieval system in which retrieval techniques for text and image documents are combined in a novel approach. In the first pass, the text-based initial query is matched against the text captions of the images in the database to obtain the initial retrieved set. In the second pass, text and image features obtained from this initial retrieved set are used to expand the initial query. Additional images from the database are then retrieved based on the expanded query. The image features that we have used are color histograms, DC coefficients from the discrete cosine transform, and two texture features: multiresolution simultaneous autoregressive model and local binary pattern. These are low-level statistical image features that can be easily computed. Extensive experiments have been performed on 1019 color pictures of mixed variety with captions, relevance judgments and queries supplied by a national archives agency. Objective precision-recall results have been obtained with various combinations of text and image features. The results show that the image features do not perform well when used on their own. However, when image features are used in query expansion, they increase the average precision more significantly than text annotations. Moreover, these findings are valid at all precision levels and are not sensitive to the image feature acquisition parameters.     

深度学习和迭代量化在图像检索中的应用研究

基于内容的图像检索的关键在于对图像进行特征提取和对特征进行多比特量化编码 。近年来,基于内容的图像检索使用低级可视化特征对图像进行描述,存在“语义鸿沟”问题;其次,传统量化编码使用随机生成的投影矩阵,该矩阵与特征数据无关,因此不能保证量化的精确度。针对目前存在的这些问题,本文结合深度学习思想与迭代量化思想,提出基于卷积神经网络VGG16和迭代量化(Iterative Quantization, ITQ)的图像检索方法。使用在公开数据集上预训练VGG16网络模型,提取基于深度学习的图像特征;使用ITQ方法对哈希哈函数进行训练,不断逼近特征与设定比特数的哈希码之间的量化误差最小值,实现量化误差的最小化;最后使用获得的哈希码进行图像检索。本文使用查全率、查准率和平均精度均值作为检索效果的评价指标,在Caltech256图像库上进行测试。实验结果表明,本文提出的算法在检索优于其他主流图像检索算法。      

Center symmetric local binary co-occurrence pattern for texture,face and bio-medical image retrieval

Content based image retrieval is a common problem for a large image database. Many methods have been proposed for image retrieval for some particular type of datasets. In the proposed work, a new image retrieval technique has been introduced. This technique is useful for different kind of dataset. In the proposed method, center symmetric local binary pattern has been extracted from the original image to obtain the local information. Co-occurrence of pixel pairs in local pattern map have been observed in different directions and distances using gray level co-occurrence matrix. Earlier methods have utilized histogram to extract the frequency information of local pattern map but co-occurrence of pixel pairs is more robust than frequency of patterns. The proposed method is tested on three different category of images, i.e., texture, face and medical image database and compared with typical state-of-the-art local patterns.     

基于内容的图像检索技术的研究与实现

如何为内容丰富多变的大量图像数据编制索引并利用该索引进行高效地相似检索是研究的核心问题。相似图像检索系统通过图像特征提取器提取图像的特征,提供访问图像内容的方法;距离函数是用来计算这些特征之间相似程度的主要工具。实验证明,该系统可以高效地为用户检索出指定特征的图像,对实际应用具有重要的价值。     

Design and analysis of a content-based pathology image retrieval system

A prototype, content-based image retrieval system has been built employing a client/server architecture to access supercomputing power from the physician's desktop. The system retrieves images and their associated annotations from a networked microscopic pathology image database based on content similarity to user supplied query images. Similarity is evaluated based on four image feature types: color histogram, image texture, Fourier coefficients, and wavelet coefficients, using the vector dot product as a distance metric. Current retrieval accuracy varies across pathological categories depending on the number of available training samples and the effectiveness of the feature set. The distance measure of the search algorithm was validated by agglomerative cluster analysis in light of the medical domain knowledge. Results show a correlation between pathological significance and the image document distance value generated by the computer algorithm. This correlation agrees with observed visual similarity. This validation method has an advantage over traditional statistical evaluation methods when sample size is small and where domain knowledge is important. A multi-dimensional scaling analysis shows a low dimensionality nature of the embedded space for the current test set.     

AN IMAGE RETRIEVAL METHOD BASED ON SPATIAL DISTRIBUTION OF COLOR

Color histogram is now widely used in image retrieval. Color histogram-based image retrieval methods are simple and efficient but without considering the spatial distribution information of the color. To overcome the shortcoming of conventional color histogram-based image retrieval methods, an image retrieval method based on Radon Transform （RT） is proposed. In order to reduce the computational complexity, wavelet decomposition is used to compress image data. Firstly, images are decomposed by Mallat algorithm. The low-frequency components are then projected by RT to generate the spatial color feature. Finally the moment feature matrices which are saved along with original images are obtained. Experimental results show that the RT based retrieval is more accurate and efficient than traditional color histogram-based method in case that there are obvious objects in images. Further more, RT based retrieval runs significantly faster than the traditional color histogram methods.     

Internet cross-media retrieval based on deep learning

With the development of Internet, multimedia information such as image and video is widely used. Therefore, how to find the required multimedia data quickly and accurately in a large number of resources, has become a research focus in the field of information process. In this paper, we propose a real time internet cross-media retrieval method based on deep learning. As an innovation, we have made full improvement in feature extracting and distance detection. After getting a large amount of image feature vectors, we sort the elements in the vector according to their contribution and then eliminate unnecessary features. Experiments show that our method can achieve high precision in image-text cross media retrieval, using less retrieval time. This method has a great application space in the field of cross media retrieval.     

A multiresolution approach for rotation invariant texture image retrieval with orthogonal polynomials model

In this paper, a simple and an efficient Content Based Image Retrieval which is based on orthogonal polynomials model is presented. This model is built with a set of carefully chosen orthogonal polynomials and is used to extract the low level texture features present in the image under analysis. The orthogonal polynomials model coefficients are reordered into multiresolution subband like structure. Simple statistical and perceptual properties are derived from the subband coefficients to represent the texture features and these features form a feature vector. The efficiency of the proposed feature vector extraction for texture image retrieval is experimented on the standard Brodatz and MIT’s VisTex texture database images with the Canberra distance measure. The proposed method is compared with other existing retrieval schemes such as Discrete Cosine Transformation (DCT) based multiresolution subbands, Gabor wavelet and Contourlet Transform based retrieval schemes and is found to outperform the existing schemes with less computational cost.     

基于二维压缩感知和分层特征的图像检索算法

为了保留图像分析时的像素点位置关系及降维处理,把一维压缩感知理论推广到二维,建立了二维可稀疏信号的压缩测量模型,研究了一种二维信号的自适应梯度下降重构AGDR(Adaptive Gradient Descent Recursion)算法,由此提出了一种图像分层特征提取与检索方法.首先对图像在RGB颜色空间上进行网格离散划分,通过分层算子对图像进行分层映射,定义一种基于颜色网格空间的扩展灰度共生矩阵,采用二维测量模型获取图像的分层测量特征、纹理特征与分层颜色统计特征,图像分层测量特征综合反映出图像的颜色及像素点位置的关系,扩展灰度共生矩阵反映纹理特征.其次用AGDR算法计算检索图像之间的原始信号差量及其稀疏值.最后结合两类分层特征差量、稀疏值和颜色统计特征,融合计算图像间整体相似度度量指标.仿真实验表明,应用分层二维压缩感知测量与AGDR算法的图像检索方法在检索时间、查全率和查准率等指标上具有优越性能,为图像检索提供了新思路.     

基于重组DCT系数子带能量直方图的图像检索

现在许多图像采用JPEG格式存储,检索这些图像通常要先解压缩,然后提取基于像素域的特征矢量进行图像检索。己有文献提出直接在DCT域进行图像检索的方法,这样可以降低检索的时间复杂度。本文提出对JPEG图像的DCT系数利用多分辨率小波变换的形式进行重组,对整个数据库中所有图像的DCT系数重组得到的若干子带,分别建立子带能量直方图,而后采用Morton顺序建立每幅图像的索引,并采用变形B树结构组织图像数据库用于图像检索。     

基于卷积神经网络和监督核哈希的图像检索方法

当前主流的图像检索方法采用的视觉特征,缺乏自主学习能力,导致其图像表达能力不强,此外,传统的特征索引方法检索效率较低,难以适用于大规模图像数据.针对这些问题,本文提出了一种基于卷积神经网络和监督核哈希的图像检索方法.首先,利用卷积神经网络的学习能力挖掘训练图像内容的内在隐含关系,提取图像深层特征,增强特征的视觉表达能力和区分性;然后,利用监督核哈希方法对高维图像深层特征进行监督学习,并将高维特征映射到低维汉明空间中,生成紧致的哈希码;最后,在低维汉明空间中完成对大规模图像数据的有效检索.在ImageNet-1000和Caltech-256数据集上的实验结果表明,本文方法能够有效地增强图像特征的表达能力,提高图像检索效率,优于当前主流方法.     

Retrieval of head–neck medical images using Gabor filter based on power-law transformation method and rank BHMT

This research aims to work on the specific medical domain. In this work, retrieval of the head–neck medical images from a database is discussed. Content-based medical image retrieval system (CBMIR) is used for retrieving the head–neck images. CBMIR is automatic and more efficient compared with the text-based approach. Shape and texture features are used for constructing feature vector. Texture feature is extracted using a modified Gabor filter based on power-law transformation method. Shape feature is extracted using rank BHMT (rank-order blur hit or miss transformation) method. Shape and texture features are combined to form a single feature vector. Threshold value very near to zero is used to retrieve images from the database. The proposed method is compared with log-Gabor filters and rank BHMT method. Combinations of modified Gabor filter with rank BHMT gave better performance than other methods.     

Image retrieval using both color and texture features

In order to improve the retrieval performance of images, this paper proposes an efficient approach for extracting and retrieving color images. The block diagram of our proposed approach to content-based image retrieval (CBIR) is given firstly, and then we introduce three image feature extracting arithmetic including color histogram, edge histogram and edge direction histogram, the histogram Euclidean distance, cosine distance and histogram intersection are used to measure the image level similarity. On the basis of using color and texture features separately, a new method for image retrieval using combined features is proposed. With the test for an image database including 766 general-purpose images and comparison and analysis of performance evaluation for features and similarity measures, our proposed retrieval approach demonstrates a promising performance. Experiment shows that combined features are superior to every single one of the three features in retrieval.