基于颜色纹理特征的SVD图像检索方法

网络等媒体中包含的图片越来越丰富,从众多的图像中查找到自己感兴趣的内容是图像处理一个重要目标.图像的颜色和纹理能从视觉上表现图像特征,因此提出了图像颜色和纹理特征融合,并用奇异值分解方法降低特征向量维度的图像检索方法.首先,提取图像LTrP(Local Tetra Patterns)纹理特征向量和HSV颜色特征向量;对图像分块,用奇异值分解方法降低图像块特征向量维度和噪声,连接图像块向量得到图像的特征向量;用欧式距离对图像进行相似性检测.实验结果表明,该方法平均检索精度明显高于其他同类检索方法.     

基于兴趣目标的图像检索

当前图像检索算法通常针对整体图像提取特征以完成检索任务.然而,在很多情况下用户只会关注图像的一部分,即他们的兴趣目标.此时,从整体图像提取的特征一部分是有效的,另一部分则是无效的且会对检索过程带来消极影响.为此,本文提出基于兴趣目标的图像检索方案,并借助于现有的显著性检测、图像分割、特征提取等技术实现一款有效的图像检索算法.首先采用HS （Hierarchical Saliency,分层显著性）检测算法分析用户的兴趣目标并应用SC （Saliency-based Image Cut,基于显著性的图像分割）算法将其分割,然后针对兴趣目标提取HSV （Hue、Saturation、Value,色调、饱和度、明度）颜色特征、SIFT （Scale Invariant Feature Transform,尺度不变特征变换）局部特征和CNN （Convolutional Neural Network,卷积神经网络）语义特征,最后计算其与数据库图像的相似度并根据相似度排序返回检索结果.仿真实验结果表明,本文算法在解决"这是什么东西"这类图像检索任务时明显优于现有的图像检索算法.     

卷积神经网络在图像目标检测中的应用

分析卷积神经网络（Convolutional Neural Networks,CNN）在图像目标检测中的性能,重点比较AlexNet、GoogleNet和ResNet50这3个流行模型在不同数据集上的表现。在CIFAR-100和CIFAR-10数据集上,GoogleNet和ResNet50表现出更精确的物体识别能力,而AlexNet相对稍弱。这些结果有助于深入了解CNN在图像识别任务中的性能和适用性。     

卷积神经网络在岩石薄片图像检索中的应用初探

近几年来,卷积神经网络引起了国内外研究者的广泛关注,并在大规模图像处理方面有出色的表现,尤其在模式识别领域.将地质勘探与计算机技术相结合,在岩石图像处理方面已经取得了较好的成绩,并且还在不断的探索中,以求更好地投入到实际中去.对于地质勘探研究者来说,对于大量的岩石薄片图像,如何进行快速并且有效的检索是值得研究的领域课题.传统的基于文本的检索方式已不能满足要求,为此,本文试图将卷积神经网络引入到岩石薄片图像的检索中,分析其在岩石薄片图像检索中的可行性.     

基于圆形感兴趣区域的图像检索算法

传统的基于内容的图像检索系统在检索时往往通过获取整幅图像的全局特征进行计算,必然含有一些冗余信息,从而给检索带来过多的计算量和不准确性。因此将检索的区域范围从全局缩小到局部,提出一种改进的图像感兴趣区域提取算法。首先使用Harris算法提取出图像的显著点,通过对显著点进行条件筛选截取出一个圆形感兴趣区域,然后对该区域提取多种特征并进行归一化处理,最后用距离函数实现图像间的相似度比较。实验结果表明,所提算法能够对图像的感兴趣区域进行有效提取,提高了运行效率,同时获得了较好的检索效果。     

基于人工神经网络的图像检索平台的设计与开发

用户描述图像的高层抽象语义与图像内在的底层特征之间存在差异,此时仅依靠图像内容特征进行检索的系统无法准确完成用户的检索任务。针对以上问题,提出了使用神经网络进行图像的匹配计算方法,通过样例自动学习和用户反馈学习两种学习方式,形成图像底层特征到图像分类的正确映射,学习后的神经网络可以进行图像的自动分类及检索。该方法结合了图像的底层特征描述及用户的高层语义反馈,有效地弥补了语义鸿沟。最后,系统通过整合Web前端、图像提取模块、神经网络模块及数据库模块,实现了神经网络学习及图像检索的完整流程。     

基于卷积神经网络和注意力机制的图像检索

当前先进的图像检索方法中,存在着不能很好地分辨图像中不同区域和内容的重要性的问题,导致计算资源分配不合理、检索正确率较低等一系列结果.为了解决这些问题,提出了一种基于卷积神经网络(Convolutional Neural Network,CNN)和注意力机制的图像检索方法.首先使用卷积神经网络提取特征,然后使用注意力机...     

结合高层语义与底层颜色特征的图像检索方法

本文提出了一种结合高层语义与底层颜色特征图像检索方法.首先使用YOLOv3目标检测算法获得图像中的目标的类别和特征信息,提取检测网络中的中间层并将中间层进行二值化作为高层语义信息;利用YOLOv3获取的目标位置信息,对图像中包含目标物体的区域进行局部颜色直方图提取;然后对整张图像进行颜色矩计算,将局部颜色直方图和颜色矩...     

Structural image retrieval using automatic image annotation and region based inverted file

Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.     

Dictionary based color image retrieval

In this work the normalized dictionary distance (NDD) is presented and investigated. NDD is a similarity metric based on the dictionary of a sequence acquired from a data compressor. A dictionary gives significant information about the structure of the sequence it has been extracted from. We examine the performance of this new distance measure for color image retrieval tasks, by focusing on three parameters: the transformation of the 2D image to a 1D string, the color to character correspondence, and the image size. We demonstrate that NDD can outperform standard (dis)similarity measures based on color histograms or color distributions.     

Spatial template extraction for image retrieval by region matching

This paper presents a template and its relation extraction and estimation (TREE) algorithm for indexing images from picture libraries with more semantics-sensitive meanings. This algorithm can learn the commonality of visual concepts from multiple images to give a middle-level understanding about image contents. In this approach, each image is represented by a set of templates and their spatial relations as keys to capture the essence of this image. Each template is characterized by a set of dominant regions, which reflect different appearances of an object at different conditions and can be obtained by the template extraction and analysis (TEA) algorithm through region matching. The spatial template relation extraction and measurement (STREAM) algorithm is then proposed for obtaining the spatial relations between these templates. Due to the nature of a template, which can represent object's appearances at different conditions, the proposed approach owns better capabilities and flexibilities to capture image contents than traditional region-based methods. In addition, through maintaining the spatial layout of images, the semantic meanings of the query images can be extracted and lead to significant improvements in the accuracy of image retrieval. Since no time-consuming optimization process is involved, the proposed method learns the visual concepts extremely fast. Experimental results are provided to prove the superiority of the proposed method.     

Hierarchical color image region segmentation for content-basedimage retrieval system

In this work, we propose a model of a content-based image retrieval system by using the new idea of combining a color segmentation with relationship trees and a corresponding tree-matching method. We retain the hierarchical relationship of the regions in an image during segmentation. Using the information of the relationships and features of the regions, we can represent the desired objects in images more accurately. In retrieval, we compare not only region features but also region relationships.     

基于多示例学习的图像检索方法

多示例学习对处理各类歧义问题有较好的效果,将它应用于周像检索问题,提出了一种新的基于多示例学习的图像检索方法。首先提取每幅图像的局部区域特征,通过对这些特征聚类求得一组基向量,并利用它们对每个局部特征向量进行编码,接着使用均值漂移聚类算法对图像进行分割,根据局部特征点位置所对应的分割块划分特征编码到相应的子集,最后将每组编码子集聚合成一个向量,这样每幅图像对应一个多示例包。根据用户选择的图像生成正包和反包,采用多示例学习算法进行学习,取得了较为满意的结果。     

Hadoop分布式的海量图像检索

传统基于内容的图像检索方法通过相似度测量算法获取检索结果,对海量图像存在检索效率低和精度差的弊端,因此设计基于Hadoop分布式的海量图像检索方法,其基于Hadoop云平台对海量数码图像实施分布式运算,采集图像SURF特征,采用K-Means聚类方法将相似图像SURF特征聚集起来,通过TF-IDF数据挖掘技术对图像特征实施量化,进而基于Hadoop平台中的Lucene框架塑造海量图像数据的索引模块和搜索模块,依据用户输入的图像SURF特征塑造海量图像数据索引,完成相似图像的准确检索。实验结果说明,所提图像检索方法检索出的图像质量佳,对海量图像进行检索的效率和精度高。     

基于感兴趣区域的图像检索方法

提出了一种新的基于感兴趣区域的图像检索算法。首先基于多曲率多项式提取图像显著点,并依据显著点提取图像感兴趣区域,然后基于感兴趣区域的颜色和纹理特征进行图像检索。实验结果表明该方法可有效地提取图像感兴趣区域,并取得了较好的检索效果。     

Histological image retrieval based on semantic content analysis

The demand for automatic recognition and retrieval of medical images for screening, reference, and management is increasing. We present an intelligent content-based image retrieval system called I-Browse, which integrates both iconic and semantic content for histological image analysis. The I-Browse system combines low-level image processing technology with high-level semantic analysis of medical image content through different processing modules in the proposed system architecture. Similarity measures are proposed and their performance is evaluated. Furthermore, as a byproduct of semantic analysis, I-Browse allows textual annotations to be generated for unknown images. As an image browser, apart from retrieving images by image example, it also supports query by natural language.     

基于局部二值模式的医学图像检索

提出了一种基于局部二值模式(LBP)和纹理模式统计进行医学图像检索的方法,计算了LBP和局部方差的联合直方图,改进了Log-likelihood统计距离度量算法.通过仿真表明:改进的Log-likelihood统计算法比Log-likelihood统计算法检索准确率高:与基于Gabor纹理特征图像检索相比较,该局部二值纹理模式检索算法检索准确率能提高8%以上.