一种基于区域的动态分块图象检索方法

本文提出一种新颖的基于内容和图像检索方法，基于运动子块分割并根据视觉特性对不同区域做不同的加权，比较各子块相似度，分析相似度矩阵，并检索查询物体。通过将图象分割细化，充分利用了原图的颜色位置信息，通过实验，实现了对特定物体进行检索。该物体检索方法可进一步发展，为特定的后续处理奠定基础，如在人脸识别等功能中发挥重要作用。     

A CBIR system based on saliency driven local image features and multi orientation texture features

In Content-based Image Retrieval (CBIR), the user provides the query image in which only a selective portion of the image carries the foremost vital information known as the object region of the image. However, the human visual system also focuses on a particular salient region of an image to instinctively understand its semantic meaning. Therefore, the human visual attention technique can be well imposed in the CBIR scheme. Inspired by these facts, we initially utilized the signature saliency map-based approach to decompose the image into its respective main object region (ObR) and non-object region (NObR). ObR possesses most of the vital image information, so block-level normalized singular value decomposition (SVD) has been used to extract salient features of the ObR. In most natural images, NObR plays a significant role in understanding the actual semantic meaning of the image. Accordingly, multi-directional texture features have been extracted from NObR using Gabor filter on different wavelengths. Since the importance of ObR and NObR features are not equal, a new homogeneity-based similarity matching approach has been devised to enhance retrieval accuracy. Finally, we have demonstrated retrieval performances using both the combined and distinct ObR and NObR features on seven standard coral, texture, object, and heterogeneous datasets. The experimental outcomes show that the proposed CBIR system has a promising retrieval efficiency and outperforms various existing systems substantially.     

基于内容的图像检索

基于内容的图像检索技术在数字图书馆、网络信息安全、预防犯罪、知识产权、医疗诊断、地理信息系统及遥感等领域有着广泛的应用。他是目前图像检索技术中比较前沿的研究热点。对基于内容的检索技术的研究意义和研究现状进行了阐述;着重介绍了基于内容(颜色、纹理、形状)的图像检索技术;最后介绍了基于内容的图像检索的发展方向。     

基于内容的图像检索与MPEG-7

随着数字图像信息的高速膨胀与增加,基于内容的图像检索技术已经成为了一个被广泛关注的研究领域.与此同时,国际标准MPEG-7的制定,极大的促进了这一领域的发展.本文将主要对基于内容的图像检索的研究现状以及关键技术进行了分析,介绍了MPEG-7标准的相关内容,并在此基础上提出一种基于MPEG-7标准的图像检索系统模型.     

A similarity learning approach to content-based image retrieval: application to digital mammography

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.     

基于内容的图像检索方法

综述了基于内容检索技术的进展,并对其主要方法如基于颜色、形状、纹理等图像检索技术进行了论述,介绍了几个典型的基于内容的图像检索系统.通过综述指出了今后的研究方向.     

基于内容的图像检索技术研究

在对海量的图像数据进行检索时,传统的基于数值/字符的信息检索技术并不能满足要求.因此,基于内容的图像检索技术(CBIR:Content-Based Image Retrieval)的研究应运而生,并引起了广泛关注.本文主要讨论CBIR研究中的一些关键问题:图像的内容特征及其提取、特征之间的相似度计算、查询条件的表达、检索性能的评价、压缩域的图像检索技术等等,并指出了一些可值得深入研究的方向.     

Hybrid textual-visual relevance learning for content-based image retrieval

Learning effective relevance measures plays a crucial role in improving the performance of content-based image retrieval (CBIR) systems. Despite extensive research efforts for decades, how to discover and incorporate semantic information of images still poses a formidable challenge to real-world CBIR systems. In this paper, we propose a novel hybrid textual-visual relevance learning method, which mines textual relevance from image tags and combines textual relevance and visual relevance for CBIR. To alleviate the sparsity and unreliability of tags, we first perform tag completion to fill the missing tags as well as correct noisy tags of images. Then, we capture users’ semantic cognition to images by representing each image as a probability distribution over the permutations of tags. Finally, instead of early fusion, a ranking aggregation strategy is adopted to sew up textual relevance and visual relevance seamlessly. Extensive experiments on two benchmark datasets well verified the promise of our approach.     

Image annotation based on multi-view robust spectral clustering

Nowadays, image annotation has been a hot topic in the semantic retrieval field due to the abundant growth of digital images. The purpose of these methods is to realize the content of images and assign appropriate keywords to them. Extensive efforts have been conducted in this field, which effectiveness is limited between low-level image features and high-level semantic concepts. In this paper, we propose a Multi-View Robust Spectral Clustering (MVRSC) method, which tries to model the relationship between semantic and multi-features of training images based on the Maximum Correntropy Criterion. A Half-Quadratic optimization framework is used to solve the objective function. According to the constructed model, a few tags are suggested based on a novel decision-level fusion distance. The stability condition and bound calculation of MVRSC are analyzed, as well. Experimental results on real-world Flickr and 500PX datasets, and Corel5K confirm the superiority of the proposed method over other competing models.     

Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking

A new algorithm meant for content based image retrieval (CBIR) and object tracking applications is presented in this paper. The local region of image is represented by local maximum edge binary patterns (LMEBP), which are evaluated by taking into consideration the magnitude of local difference between the center pixel and its neighbors. This LMEBP differs from the existing LBP in a manner that it extracts the information based on distribution of edges in an image. Further, the effectiveness of our algorithm is confirmed by combining it with Gabor transform. Four experiments have been carried out for proving the worth of our algorithm. Out of which three are meant for CBIR and one for object tracking. It is further mentioned that the database considered for first three experiments are Brodatz texture database (DB1), MIT VisTex database (DB2), rotated Brodatz database (DB3) and the fourth contains three observations. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and other existing transform domain techniques.     

Content-based image retrieval using local visual attention feature

Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, salient point based image retrieval has attracted many researchers. However, the related work is usually very time consuming, and some salient points always may not represent the most interesting subset of points for image indexing. Based on fast and performant salient point detector, and the salient point expansion, a novel content-based image retrieval using local visual attention feature is proposed in this paper. Firstly, the salient image points are extracted by using the fast and performant SURF (Speeded-Up Robust Features) detector. Then, the visually significant image points around salient points can be obtained according to the salient point expansion. Finally, the local visual attention feature of visually significant image points, including the weighted color histogram and spatial distribution entropy, are extracted, and the similarity between color images is computed by using the local visual attention feature. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal.     

Pictures are not taken in a vacuum - an overview of exploiting context for semantic scene content understanding

Considerable research has been devoted to the problem of multimedia indexing and retrieval in the past decade. However, limited by state-of-the-art in image understanding, the majority of the existing content-based image retrieval (CBIR) systems have taken a relatively low-level approach and fallen short of higher-level interpretation and knowledge. Recent research has begun to focus on bridging the semantic and conceptual gap that exists between man and computer by integrating knowledge-based techniques, human perception, scene content understanding, psychology, and linguistics. In this article, we provide an overview of exploiting context for semantic scene content and understanding     

一种改进的颜色匹配对图像检索算法

以子块直方图彩色图像检索算法为基础， 分析了进一步利用图像空间相似信息的颜色匹配对检索算法的性能。在子块直方图的构成、直方图距离值的归类等方面提出了行之有效的改进方法；给出了子块大小、相似度阈值等参数选择的优化原则，使查准率、查全率等检索性能指标得到了较大的提高，得出了几个有用的结论并形成了实验系统。     

利用图像纹理特征的图像检索

随着多媒体技术的发展，大容量图像库得到了广泛的应用，基于内容的图像检索（CBIR）技术则是进行管理和检索的有效手段。介绍了利用图像的纹理特征进行图像检索的方法、具体算法和CBIR系统的实现，给出了试验结果。     

基于彩色空间3D广义共发矩阵的视觉信息检索

本文针对基于内容的图象检索（CBIR）提出一组基于HSV空间的3D广义共发矩阵的新颖的图象纹理特征，通过在CBIR检索系统iPhoto中（25，000张图象）上测试，利用本文特征地传统灰度共发矩阵。     

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.     

Intermediate Annotationless Dynamical Object-Index-Based Query in Large Image Archives with Holographic Representation

This paper presents a new parallel and distributed associative network-based technique for content-based image retrieval (CBIR) with dynamic indices. Unlike any prior artificial associative networks (AAM), this new associative search network has the unique ability to explicitly focus on any subset of pixels in the image. It can also provide a feedback meta-quantity on the quality of outgoing information. The network is founded on a bi-modal representation of information elements which in addition to basic information also includes meta-states. Its computational model has been derived from optical holography. These unique capabilities coupled with usual advantages of associative computing (adaptability, efficiency, ability to cope with imprecision, parallel and distributed mode of computation) now for the first time make it possible to realize a CBIR technique based on associative computing. This new CBIR strategy provides an inquirer greater flexibility to independently and dynamically construct object-indices without depending on the fixed, predefined ad hoc indices used by traditional CBIR approaches. The paper presents the mechanism, architecture, and performance of an image archival and retrieval system realized with this new network.     

基于多尺度相位特征的图像检索方法

在基于内容的图像检索中,一个关键的问题是图像视觉内容的表述。而传统的颜色,形状和纹理特征对于图像内容的表述尚且不够完备。为进一步提高检索准确率,针对人眼视觉特性,该文提出了一种基于多尺度相位特征的图像检索方法。该方法首先采用尺度空间理论得到图像的多尺度描述,然后通过复数可调滤波(complex steerable filtering)提取图像的多尺度相位信息并利用直方图投影获取全局统计的多尺度相位特征。在通用数据库COREL 5000上的实验结果表明,该特征相对经典的颜色特征提高至少5%检索准确率,且能对之提供有效补充。     

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.