小波尺度空间形状特征提取方法的研究

基于形状的图像检索技术是基于内容的图像检索技术的一个重要组成部分。现有的形状特征检索技术主要集中于形状特征的提取及相似性度量、形状特征与颜色和纹理特征结合、形状特征与高层的语义特征结合的研究。在分析现有的基于形状的图像检索技术的一些关键技术的基础上,对基于小波尺度空间特征(WSS)的形状检索方法进行了研究,并提出了一些改进算法。     

基于多特征的服装图像检索

基于内容的图像检索一直是一个受关注的研究热点,这里利用图像的颜色和形状特征,将基于内容的图像检索应用于电子购物领域。提出先利用不变距与傅里叶描述子相结合的方法对图像形状特征进行检索,再利用改进的颜色直方图进行二次检索的检索方法。在检索前引入图像背景消除法消除图像中背景信息的影响。最后通过实验验证了基于颜色和形状特征的服装图像检索效果以及利用图像背景去除对检索效果的影响。     

一个基于MPEG-7颜色与形状的图像检索系统

结合MPEG-7的颜色与形状两种描述符设计出了一个新的图像检索系统,利用MPEG-7提供的主颜色描述符及基于区域的形状描述算法--角放射变换(ART)对图像进行特征提取、描述.实验用MPEG-7的评价准则进行评判,由包括花卉、自然风景及商标组成的图像库进行测试,结果表明,综合这两种特征进行检索比使用单一特征取得的检索效果更佳.     

基于边缘方向直方图的形状检索

近年来,随着多媒体技术和数字设备的出现,如何有效地管理和访问图像信息已成为人们亟待解决的问题.因此,一种新的图像检索技术——基于内容的图像检索技术被提出来.其中,由于图像的形状特征更符合人们的视觉感知,因此基于形状的图像检索越来越受到研究者的关注.旨在研究基于形状轮廓特征的图像检索,提出了基于边缘方向的直方图形状检索算法.通过对常用边缘检测算子的分析和比较,给出了边缘方向直方图特征提取的具体实现技术,对采用的特征匹配方法做了描述,最后通过实验的结果与分析验证了算法的性能.     

融合颜色与形状特征的图像检索方法

基于颜色或颜色-空间信息的图像检索方法，由于没有考虑图像中所含目标对象的形状特征，检索效果往往不够理想，针对这一不足。文章提出并设计了颜色-梯度方向角二维直方图，将图像的颜色特征与形状特征融合起来进行图像检索。试验结果表明，该方法的检索精度与效率都有明显的提高。     

用于图像检索的MPEG-7形状描述子

介绍了基于内容的图像检索技术的研究现状，并对将MPEG-7形状描述子应用于图像数据库进行图像检索进行了研究，最后提出了一些需要解决的问题和今后的研究方向。     

基于Nonsubsampled Contourlet变换的SAR图像形状特征检索

Nonsubsampled Contourlet变换是一种非抽取得具有平移不变性的多尺度多方向的变换。将Canny算子和Nonsubsampled Contourlet变换(NSCT)[1]相结合,对图像运用Canny算子[2]提取边缘特征,再进行Nonsubsampled Contourlet变换,引入了三阶中心矩作为特征向量提取形状特征的算法。实现了基于Nonsubsampled Contourlet变换的图像形状特征检索,并将结果与基于2-D小波变换和基于Contourlet变换的图像形状特征检索作了比较,实验结果证明该方法的图像形状特征检索效率有较大的提高。     

基于多特征的对象检索技术

提出了一种融合图像颜色、纹理和形状特征的提取及归一化方法,并将其应用于基于内容的对象检索中,实验证明,融合颜色、纹理、和形状特征的对象检索比单一特征的对象检索效果要好。     

一种综合PHOG形状和小波金字塔能量分布特征的图像检索方法

形状是物体的一个重要属性,在图像检索中发挥着重要作用.PHOG梯度方向直方图金字塔是最近出现的一种表达力较强的形状特征,但其对自然背景图像的检索效果不佳.图像的小波分解能得到图像能量场在各频域的分布,从而可用于图像检索.本文提出一种新的小波金字塔能量分布特征,在基于SVM分类器的检索框架下,与图像的PHOG形状描述特征...     

一种用于形状描述的拱高半径复函数

提出了一种新的用于形状描述的轮廓线函数-拱高半径复函数(AHRC).AHRC用中心距离和带正负号的拱高来分别描述形状的全局特征和局部细节.用AHRC的傅立叶变换系数构成描述形状的特征向量.在MPEG-7标准测试集上对该方法进行图像检索实验,并将其实际应用于植物叶片图像的检索,同现有的分别基于中心距离、三角形面积、最远点...     

Integration of wavelet transform,Local Binary Patterns and moments for content-based image retrieval

The proliferation of large number of images has made it necessary to develop systems for indexing and organizing images for easy access. This has made Content-Based Image Retrieval (CBIR) an important area of research in Computer Vision. This paper proposes a combination of features in multiresolution analysis framework for image retrieval. In this work, the concept of multiresolution analysis has been exploited through the use of wavelet transform. This paper combines Local Binary Pattern (LBP) with Legendre Moments at multiple resolutions of wavelet decomposition of image. First, LBP codes of Discrete Wavelet Transform (DWT) coefficients of images are computed to extract texture feature from image. The Legendre Moments of these LBP codes are then computed to extract shape feature from texture feature for constructing feature vectors. These feature vectors are used to search and retrieve visually similar images from large database. The proposed method has been tested on five benchmark datasets, namely, Corel-1K, Olivia-2688, Corel-5K, Corel-10K, and GHIM-10K, and performance of the proposed method has been measured in terms of precision and recall. The experimental results demonstrate that the proposed method outperforms some of the other state-of-the-art methods in terms of precision and recall.     

Content-based image retrieval using a fusion of global and local features

Color, texture, and shape act as important information for images in human recognition. For content-based image retrieval, many studies have combined color, texture, and shape features to improve the retrieval performance. However, there have not been many powerful methods for combining all color, texture, and shape features. This study proposes a content-based image retrieval method that uses the combined local and global features of color, texture, and shape. The color features are extracted from the color autocorrelogram; the texture features are extracted from the magnitude of a complete local binary pattern and the Gabor local correlation revealing local image characteristics; and the shape features are extracted from singular value decomposition that reflects global image characteristics. In this work, an experiment is performed to compare the proposed method with those that use our partial features and some existing techniques. The results show an average precision that is 19.60% higher than those of existing methods and 9.09% higher than those of recent ones. In conclusion, our proposed method is superior over other methods in terms of retrieval performance.     

基于方块编码的图像纹理特征提取及检索算法

针对灰度共生矩阵（GLCM）在提取纹理特征时存在的问题,提出一种基于方块编码（BTC）的图像纹理特征的检索算法。首先将图像分成互不重叠的子图像块,然后利用BTC的思想对这些图像块进行编码,进而定义图像的纹理基元并以此作为对图像的纹理描述,并提出采用一种改进的基于纹理基元的共生矩阵来获取纹理特征。实验结果表明,该方法既有效地利用了图像的纹理信息,又考虑了图像的空间和形状信息,具有较好的检索效果。     

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.     

Multi-object image retrieval based on shape and topology

We aim at developing a geometry-based retrieval system for multi-object images. We model both shape and topology of image objects including holes using a structured representation called curvature tree (CT); the hierarchy of the CT reflects the inclusion relationships between the objects and holes. To facilitate shape-based matching, triangle-area representation (TAR) of each object and hole is stored at the corresponding node in the CT. The similarity between two multi-object images is measured based on the maximum similarity subtree isomorphism (MSSI) between their CTs. For this purpose, we adapt a continuous optimization approach to solve the MSSI problem and a very effective dynamic programming algorithm to measure the similarity between the attributed nodes. Our matching scheme agrees with many recent findings in psychology about the human perception of multi-object images. Experiments on a database of 1500 logos and the MPEG-7 CE-1 database of 1400 shape images have shown the significance of the proposed 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。     

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.     

一种改进的综合BTC颜色矩和DT-CWT的图像检索算法

摘 要：特征提取是基于内容的图像检索中的关键技术。针对基于单一特征检索效果不理想的问题,提出一种改进的综合颜色和纹理特征的图像检索算法。该算法在YIQ颜色空间中进行特征提取,首先结合方块编码（BTC）的思想,提取颜色矩作为颜色特征;采用双树复小波变换（DT-CWT）提取纹理特征,融合两种特征并利用相似性度量方式进行图像检索。实验结果表明算法所提取的颜色、纹理特征更利于检索,使用综合特征检索的平均查准率比同类算法更高。