基于卷积神经网络的鸟类视频图像检索研究

本文介绍了在基于卷积神经网络的图像检索分析研究中,针对青海湖野鸟监控的视频关键帧图像数据,首先我们采用Vgg16神经网络的预训练模型来提取图像的特征,然后将特征向量作为图像的代表信息进行存储,构造图像的特征向量空间。之后对存储的高维特征向量进行降维处理,用主成分分析的方法将特征向量映射到低维的向量空间。最后应用余弦相似度算法对查询图像的特征向量与特征库中向量进行匹配,实现相似图像的检索。本文对提出的方法进行了实验,通过特征提取和恰当的特征降维,测试数据的检索准确率达到了89.82%。实验表明,本文提出的方法可以有效的实现鸟类图像的相似性检索。     

基Web的图像搜索

介绍一种基于Web的图像搜索技术，提出了作者设计的图像检索的模型，阐述了Web页中图像与文本之间的关系，对相似度的计算、词条的匹配算法、检索的反馈技术等作了详细的论述，并通过实验检验搜索模型，结论表明图像搜索效率高。     

保局投影算法在图像检索中的应用研究*

对国际上近来提出的保局投影（LPP）算法在图像检索中的最新应用研究进行了详细介绍;分析指明了几种基于LPP的图像检索算法的特点;设计并完成了基于LPP的图像检索算法图像检索效果的比较实验;最后根据实验结果总结了各类算法的优缺点。     

一种基于降维技术的图像局部融合算法

给出了一种基于降维技术的局部融合算法，并将其用于两幅灰度图像的融合。该算法将现有的图像融合技术和非线性降维技术融合，通过对待融合图像进行分块、关联矩阵表示、MDS降维处理，得到待融合图像的感兴趣区域块，对这些感兴趣区域块进行基于小波的局部融合，使图像变小。通过实验证明，该算法提高了融合速度，有利于目标识别、变换检测等后续处理。     

基于鲁棒的余弦欧氏距离度量降维的图像检索方法

为解决主成分分析（PCA）无法处理非线性数据集以及鲁棒性差的问题,提出一种鲁棒的余弦-欧氏距离度量的降维方法（RCEM）。该方法利用余弦度量（CM）能够处理离群点的特点来提取数据的局部几何特征,并利用欧氏距离能够很好地保持样本的方差信息的特点来刻画数据集的全局分布,在保留数据局部信息的同时实现了局部和全局的统一,提高了局部降维算法的鲁棒性,同时避免了局部小样本问题。实验结果显示,与角度优化全局嵌入（AOGE）方法相比,在Corel-1000数据集下检索查准率提高了5.61%,相比不降维时检索时间减少了42%。结果表明,RCEM算法能在不降低图像检索精度的同时提高图像检索的效率,可以有效应用于基于内容的图像检索（CBIR）。     

结合自适应融合网络与哈希的图像检索算法

卷积神经网络因其对图像识别准确率高而在图像检索领域备受青睐,但处理大规模数据集时,基于卷积神经网络提取的深度特征维度高,容易引发"维度灾难".针对图像检索中深度特征维度高的问题,提出一种基于自适应融合网络特征提取与哈希特征降维的图像检索算法.由于传统哈希处理高维特征复杂度高,因此本文在卷积神经网络中加入自适应融合模块对特征进行重新整合,增强特征表征能力的同时降低特征维度;然后应用稀疏化优化算法对深度特征进行第2次降维,并通过映射获得精简的哈希码;最后,实验以Inception网络作为基础模型,在数据集CIFAR-10和ImageNet上进行了丰富的实验.实验结果表明,该算法能有效提高图像检索效率.     

基于局部保持映射的图像隐密检测算法

提出了一种新的基于局部保持映射（Locality Preserving Projections,LPP）降维的图像隐密检测方案。为降低图像特征向量的维数,同时保持其内在低维结构,方便构造更有效的分类器,在经过小波变换形成图像特征后,利用LPP算法得到图像特征集的低维流形,实现对图像高维特征的降维。进而使用支持向量机（SVM）算法将降维后的特征映射到分类特征空间,实现对正常图像和隐密图像分类。实验结果表明,与不采用降维算法的检测方案相比,提出的方案能够显著地提高检测的准确率。     

一种基于可拓理论的图像检索方法

图像检索技术发展已经有一定的历史,基于文本的检索技术逐渐成熟,但是基于内容的图像检索技术还在探索阶段,本文在简单的介绍图像检索技术的基础上,提出一种可拓方法应用于图像检索的的图像检索方法,并简单的分析了基于颜色,纹理和图像轮廓的检索算法,最后通过实验比较了各算法的效果。     

基于图像分块的改进Fisher人脸识别算法

二维方法用于图像矩阵特征提取,虽然速度快,但影响了分类速度。针对二维线性鉴别分析（Two-Dimensional Linear Discriminant Analysis,2DLDA）的特点,研究了一种基于图像分块的改进Fisher人脸识别算法,该算法首先对人脸图像进行压缩降维处理,得到相应的特征矩阵,然后利用改进Fisher算法对特征矩阵进行类间离散度矩阵和类内离散度矩阵的计算,该算法充分考虑了类别信息,避免了传统Fisher算法造成的小样本问题,有效提高了分类速度。基于ORL（Olivetti Research Laboratory）与Yale人脸数据库的实验结果证明了该算法的有效性。     

基于内容图像检索中的索引技术*

首先总结了基于内容图像检索中索引技术的研究现状,指出了其中存在的问题以及今后发展趋势,然后提出了一个新的聚类算法和降维算法,并将两者结合起来提出了一个可用于基于内容图像检索的索引机制。     

高光谱遥感影像降维的拉普拉斯特征映射方法

针对高光谱遥感影像数据量大、数据冗余度高的特点,引入拉普拉斯特征映射方法对高光谱遥感数据进行非线性降维。为了解决传统流形学习方法不能处理大数据量遥感影像的问题,本文提出了基于多元线性回归的拉普拉斯特征映射线性解法。实验证明,本文提出的降维方法能够保持数据集在原始特征空间分布的局部几何属性,降维后的影像具有更好的分类精度。     

Image retrieval using nonlinear manifold embedding

The huge number of images on the Web gives rise to the content-based image retrieval (CBIR) as the text-based search techniques cannot cater to the needs of precisely retrieving Web images. However, CBIR comes with a fundamental flaw: the semantic gap between high-level semantic concepts and low-level visual features. Consequently, relevance feedback is introduced into CBIR to learn the subjective needs of users. However, in practical applications the limited number of user feedbacks is usually overwhelmed by the large number of dimensionalities of the visual feature space. To address this issue, a novel semi-supervised learning method for dimensionality reduction, namely kernel maximum margin projection (KMMP) is proposed in this paper based on our previous work of maximum margin projection (MMP). Unlike traditional dimensionality reduction algorithms such as principal component analysis (PCA) and linear discriminant analysis (LDA), which only see the global Euclidean structure, KMMP is designed for discovering the local manifold structure. After projecting the images into a lower dimensional subspace, KMMP significantly improves the performance of image retrieval. The experimental results on Corel image database demonstrate the effectiveness of our proposed nonlinear algorithm.     

自适应邻域选择的数据可分性降维方法

针对现有基于流形学习的降维方法对局部邻域大小选择的敏感性,且降至低维后的数据不具有很好的可分性,提出一种自适应邻域选择的数据可分性降维方法。该方法通过估计数据的本征维度和局部切方向来自适应地选择每一样本点的邻域大小;同时,使用映射数据时的聚类信息来汇聚相似的样本点,保证降维后的数据具有良好的可分性,使之实现更好的降维效果。实验结果表明,在人工生成的数据集上,新方法获得了较好的嵌入结果;并且在人脸的可视化分类和图像检索中得到了期望的结果。     

Learning a hierarchical image manifold for Web image classification

Image classification is an essential task in content-based image retrieval.However,due to the semantic gap between low-level visual features and high-level semantic concepts,and the diversification of Web images,the performance of traditional classification approaches is far from users’ expectations.In an attempt to reduce the semantic gap and satisfy the urgent requirements for dimensionality reduction,high-quality retrieval results,and batch-based processing,we propose a hierarchical image manifold with novel distance measures for calculation.Assuming that the images in an image set describe the same or similar object but have various scenes,we formulate two kinds of manifolds,object manifold and scene manifold,at different levels of semantic granularity.Object manifold is developed for object-level classification using an algorithm named extended locally linear embedding(ELLE) based on intra-and inter-object difference measures.Scene manifold is built for scene-level classification using an algorithm named locally linear submanifold extraction(LLSE) by combining linear perturbation and region growing.Experimental results show that our method is effective in improving the performance of classifying Web images.     

Image retrieval based on incremental subspace learning

Many problems in information processing involve some form of dimensionality reduction, such as face recognition, image/text retrieval, data visualization, etc. The typical linear dimensionality reduction algorithms include principal component analysis (PCA), random projection, locality-preserving projection (LPP), etc. These techniques are generally unsupervised which allows them to model data in the absence of labels or categories. In this paper, we propose a semi-supervised subspace learning algorithm for image retrieval. In relevance feedback-driven image retrieval system, the user-provided information can be used to better describe the intrinsic semantic relationships between images. Our algorithm is fundamentally based on LPP which can incorporate user's relevance feedbacks. As the user's feedbacks are accumulated, we can ultimately obtain a semantic subspace in which different semantic classes can be best separated and the retrieval performance can be enhanced. We compared our proposed algorithm to PCA and the standard LPP. Experimental results on a large collection of images have shown the effectiveness and efficiency of our proposed algorithm.     

利用分形编码进行基于形状的图像检索

分形编码在图像压缩方面取得了很好的效果,同时也能够用于基于内容的图像检索。该文结合分形技术,提出了一种有效的抽取特征检索图像的新方法。针对目前基于形状的图像检索领域中存在的问题,首先提出了一种通过构造不规则区域的相似膨胀分形编码算法,该算法能获得较高的压缩比。在图像检索中,该算法用一新的方式提取形状特征,实现了基于该算法的图像检索操作。实验结果表明该文算法能较好地描述图像的形状及空间分布信息,明显地提高了检索效率及准确度。     

NMF和Isomap相结合的图像检索新方法*

非负矩阵分解(NMF)能够提取图像的局部特征,是一种基于局部的数据挖掘方法,在一定程度上勾勒出了相关图像在基矩阵所代表空间上的分布,然而NMF并未考虑数据的内在几何结构。提出了一种新颖的基于非负矩阵分解和非线性降维方法Isomap相结合的新方法,全局的非线性降维方法Isomap能发现数据的内在结构和相关性,使高维数据在低维空间变得可视化。将本算法应用于图像检索,实验表明,该方法能够更加准确的获取信息,提高检索的准确性。     

A new matching strategy for content based image retrieval system

Adopting effective model to access the desired images is essential nowadays with the presence of a huge amount of digital images. The present paper introduces an accurate and rapid model for content based image retrieval process depending on a new matching strategy. The proposed model is composed of four major phases namely: features extraction, dimensionality reduction, ANN classifier and matching strategy. As for the feature extraction phase, it extracts a color and texture features, respectively, called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). However, integrating multiple features can overcome the problems of single feature, but the system works slowly mainly because of the high dimensionality of the feature space. Therefore, the dimensionality reduction technique selects the effective features that jointly have the largest dependency on the target class and minimal redundancy among themselves. Consequently, these features reduce the calculation work and the computation time in the retrieval process. The artificial neural network (ANN) in our proposed model serves as a classifier so that the selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image. In addition, the proposed model presents an effective feature matching strategy that depends on the idea of the minimum area between two vectors to compute the similarity value between a query image and the images in the determined class. Finally, the results presented in this paper demonstrate that the proposed model provides accurate retrieval results and achieve improvement in performance with significantly less computation time compared with other models.     

一种基于PCA的图像表示与检索技术

本文提出了一种新的基于图像颜色分布特征和空间位置特征的图像表示方法以及相应的检索技术，首先使用一个颜色匀质谓词对图像进行多层分解，然后从分解得到的子图像中导出图像特征：“阈值子特征”和“颜色匀质标志子特征”，由于这种图像特征的给数将随着图像分解层数的增加而迅速膨胀，为此利用主分量分析法对其降维，图像的相似度量定义为归一化的子特征间的欧氏距离的线性组合，实验结果表明：使用本文提出的方法获得的图像检索结果能较好地同人们的期望结果保持一致。