一种基于区域分割和相关反馈的图像检索方法

提出了一种综合利用区域分割和相关反馈两者优点的图像检索算法。用区域分割有效获取图像底层特征,以支持向量机（SVM）分类器构成非对称包支持向量机（AB—SVM）多分类器组合,从而解决相关反馈中样本不足的问题。实验结果表明,文中方法提高了初次检索和相关反馈的性能。     

基于主动学习的相关反馈算法研究

相关反馈(reference feedback)是信息检索领域中一种常用技术,近年来,该技术被广泛应用与基于内容的图像检索(CBIR)领域中,旨在通过用户与图像检索系统间的交互过程,克服图像底层特征与高层语义之间的语义鸿沟问题。将主动学习算法结合到相关反馈技术当中,其目的是利用主动学习算法,从无标记图像集中选择最具有信息化的部分图像作为反馈图像,减少用户与系统之间的反馈次数。在COREL图像库和VOC图像库上,对基于主动学习的相关反馈技术进行实验验证,实验结果证明了,基于主动学习的相关反馈技术可以有效提高图像检索系统的性能。     

一种SVM集成的图像分类方法研究*

针对单分类器没有充分考虑数据集的特征而不能很好地完成分类识别,提出了一种基于集成学习技术的SVM集成的图像分类方法。该方法是在基于较为流行的词袋(Bag-of-Words,BOW)模型的图像分类方法的基础上,利用训练生成的不同SVM分类器分类测试图像,并将分类结果采用集成学习算法进行集成。分别采用传统的BOW模型的图像分类方法和本文提出的方法进行分类实验,实验结果表明采用SVM集成的图像分类方法明显提高了分类精度,具有一定的稳健性。     

一种基于SVM主动学习的卡通视频检测方法

通过分析卡通与非卡通视频在视觉上的差异,对视频片断提取了MPEG-7描述子等8组视觉特征来构造卡通视频的特征空间;并将主动相关反馈技术引入到支撑向量机(SVM)算法中,设计了一种基于主动学习的卡通视频检测分类方法。利用大量实际视频片断所做的测试实验结果表明,该文选取的特征对卡通和非卡通视频有较好的区分能力;且与单纯的SVM算法以及传统相关反馈和SVM算法结合的方法相比,该文算法在检测性能上有较大的优势。     

基于教与学优化算法的相关反馈图像检索

为提高基于内容的图像检索的检索性能和检索速度,克服低层视觉特征与高层语义概念间的“语义鸿沟”,提出一种基于教与学优化的图像检索相关反馈算法（TLBO-RF）.结合图像检索问题的特殊性和粒子群优化算法的优点,对TLBO算法中个体的更新机制进行了改进,通过将相关图像集的中心作为教师以及引入学员最好学习状态Pbest,使之朝用户感兴趣的相关图像区域快速收敛.将该算法与目前效果最好的两种基于进化算法的相关反馈技术在两套标准图像测试集上进行对比,结果表明本文算法相较于另外两种算法具有明显的优势,不仅提高了图像检索性能,同时也加快了图像检索速度,更好地满足了用户的检索要求.     

基于SVM能量模型的改进主动轮廓图像分割算法研究

为克服经典主动轮廓模型曲线内外区域能量定义在复杂目标与背景分布情况下的不足,本文将高效的支持向量机有监督学习分类器引入基于Mumford-shah模型的主动轮廓图像分割算法中,提出了基于SVM能量模型的改进主动轮廓图像分割方法.该方法首先利用支持向量机的分类结果对于封闭曲线的内外区域分别构造了一种新的图像能量表示方法,因为分割过程充分利用了有监督学习策略,使得本文提出的算法具有更高的稳定性和更加广泛的适用范围,特别是对目标灰度分布不均或存在多纹理的目标也可以得到较好的分割结果.分割时,首先利用SVM实现粗分割得到目标初始轮廓,然后利用改进的Mumford-shah主动轮廓模型进行精确分割,采用粗分割策略一方面可以大大提高分割速度,另一方面也可以提高了算法的自动化程度.对比实验结果表明本文提出的算法具有更大灵活性和更好的分割性能.     

支持向量机和BP网络改进模型的性能对比研究

通过引入支持向量机（SVM）方法，提出了基于SVM的遥感图像多类分类模型，分析了SVM多类分类器的构造及其参数选取问题，并结合实例，讨论了SVM分类器性能随其本身参数变化情况，最后与几种代表性的BP网络改进模型进行了系统的对比分析。实验表明，SVM方法的分类时间要远大于改进的BP模型，而分类精度优于BP网络改进模型中效果最好的几种优化算法3个百分点左右，是一种有效的图像分类方法。     

基于散布矩阵分析的相关反馈算法及应用

相关反馈技术是一种较常用的提高信息检索精度的方法.在图像检索领域,相关反馈技术被认为是解决图像高层语义内容和低层视觉特征之间差异的一种有效方法.视觉特征的权值调整是一类应用较多的相关反馈技术,权值调整方法中存在矩阵奇异问题,本文提出了一种新的基于散布矩阵分析的相关反馈算法,解决了矩阵奇异问题.该方法通过分析与检索目标相关图像在特征空间中的散布来构造目标图像类的投影空间,该空间对应于一个高层语义类在特征空间中分布密集的子空间,在投影空间中计算相似图像;同时根据每次反馈的信息不断修正投影空间来提高系统的检索性能.在Cord图像数据库中的实验结果表明该算法具有良好的检索性能.     

基于混合核的PSO-SVM的L形天线谐振频率建模

人工神经网络进行建模时通常需要准备大量的数据样本,同时,网络结构一般都比较复杂;而采用支持向量机（SVM）进行建模时,不同核函数有不同的效果,各有利弊,且选取SVM模型参数的理论支撑尚不完整。为了解决这些问题,文中提出了一种基于粒子群优化（PSO）算法的新的SVM混合核函数,这种混合核函数是将局部核函数中的柯西核函数和全局核函数中的多项式核函数进行线性组合,且组合系数和各个核函数中的参数采用PSO算法来优化选取。采用UCI数据库中的wine-red数据集对该混合核函数进行了验证,仿真结果表明,该混合核函数可以提高模型的学习能力和泛化能力。最后,将基于混合核函数的PSO SVM方法用于L形微带天线谐振频率建模,进一步证明了这种方法是可行的和有效的。     

基于LS-SVM的氧乙炔火焰图像分类研究

氧乙炔火焰在工业生产中应用广泛,其燃烧性能是一个关乎生产效益、安全生产和环境保护的重要因素。利用图像处理技术和模式识别算法,研究氧乙炔火焰图像与其燃烧性能之间的关系,确定基于火焰图像的火焰燃烧性能评价标准,将有助于提高实际生产过程中燃烧控．1的自动化程度与燃烧性能检测的精度。本文提取了火焰图像的11种特征值作为样本,应用最小二来支持向量机（（Least Squares-Support Veotor Maohine,LS—SVM）算法,实现了对四种氧乙炔火焰图像的分类识别。通过与BP神经网络和标准SVM算法的对比实验,LS-SVM在处理速度和分类精度方面有显著优越性。     

Asymmetric Semi‐Supervised Boosting Scheme for Interactive Image Retrieval

Support vector machine (SVM) active learning plays a key role in the interactive content‐based image retrieval (CBIR) community. However, the regular SVM active learning is challenged by what we call “the small example problem” and “the asymmetric distribution problem.” This paper attempts to integrate the merits of semi‐supervised learning, ensemble learning, and active learning into the interactive CBIR. Concretely, unlabeled images are exploited to facilitate boosting by helping augment the diversity among base SVM classifiers, and then the learned ensemble model is used to identify the most informative images for active learning. In particular, a bias‐weighting mechanism is developed to guide the ensemble model to pay more attention on positive images than negative images. Experiments on 5000 Corel images show that the proposed method yields better retrieval performance by an amount of 0.16 in mean average precision compared to regular SVM active learning, which is more effective than some existing improved variants of SVM active learning.     

A unified framework for image retrieval using keyword and visual features.

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.     

Support Vector Machine Learning for Region‐Based Image Retrieval with Relevance Feedback

We present a relevance feedback approach based on multi‐class support vector machine (SVM) learning and cluster‐merging which can significantly improve the retrieval performance in region‐based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low‐level features and high‐level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re‐clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two‐class SVM and multi‐class relevance feedback methods.     

一种运动数据检索的相关反馈算法

本文提出了一种基于RankBoost的运动数据检索相关反馈算法.该算法具有以下二个方面的特点:首先,以KNN-DTW作为RankBoost集成学习的弱排序器,在适应变长多变量时间序列(Variable-Length Multivariate Time Series,VLMTS)数据的同时,利用RankBoost的集成性与高效性解决相关反馈实时性要求与VLMTS数据计算复杂度高的矛盾;其次,以本文提出的最小化排序经验损失和泛化损失风险作为RankBoost集成学习目标,有效地克服了相关反馈小样本学习环境下的过拟合问题.在CMU动作库上的实验结果验证了该方法的有效性.     

An efficient and effective region-based image retrieval framework

An image retrieval framework that integrates efficient region-based representation in terms of storage and complexity and effective on-line learning capability is proposed. The framework consists of methods for region-based image representation and comparison, indexing using modified inverted files, relevance feedback, and learning region weighting. By exploiting a vector quantization method, both compact and sparse (vector) region-based image representations are achieved. Using the compact representation, an indexing scheme similar to the inverted file technology and an image similarity measure based on Earth Mover's Distance are presented. Moreover, the vector representation facilitates a weighted query point movement algorithm and the compact representation enables a classification-based algorithm for relevance feedback. Based on users' feedback information, a region weighting strategy is also introduced to optimally weight the regions and enable the system to self-improve. Experimental results on a database of 10,000 general-purposed images demonstrate the efficiency and effectiveness of the proposed framework.     

Relevance feedback using generalized Bayesian framework with region-based optimization learning.

This paper presents a generalized Bayesian framework for relevance feedback in content-based image retrieval. The proposed feedback technique is based on the Bayesian learning method and incorporates a time-varying user model into the formulation. We define the user model with two terms: a target query and a user conception. The target query is aimed to learn the common features from relevant images so as to specify the user's ideal query. The user conception is aimed to learn a parameter set to determine the time-varying matching criterion. Therefore, at each feedback step, the learning process updates not only the target distribution, but also the target query and the matching criterion. In addition, another objective of this paper is to conduct the relevance feedback on images represented in region level. We formulate the matching criterion using a weighting scheme and proposed a region clustering technique to determine the region correspondence between relevant images. With the proposed region clustering technique, we derive a representation in region level to characterize the target query. Experiments demonstrate that the proposed method combined with time-varying user model indeed achieves satisfactory results and our proposed region-based techniques further improve the retrieval accuracy.     

A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback.

A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework.     

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.     

A unified image retrieval framework on local visual and semantic concept-based feature spaces

This paper presents a learning-based unified image retrieval framework to represent images in local visual and semantic concept-based feature spaces. In this framework, a visual concept vocabulary (codebook) is automatically constructed by utilizing self-organizing map (SOM) and statistical models are built for local semantic concepts using probabilistic multi-class support vector machine (SVM). Based on these constructions, the images are represented in correlation and spatial relationship-enhanced concept feature spaces by exploiting the topology preserving local neighborhood structure of the codebook, local concept correlation statistics, and spatial relationships in individual encoded images. Finally, the features are unified by a dynamically weighted linear combination of similarity matching scheme based on the relevance feedback information. The feature weights are calculated by considering both the precision and the rank order information of the top retrieved relevant images of each representation, which adapts itself to individual searches to produce effective results. The experimental results on a photographic database of natural scenes and a bio-medical database of different imaging modalities and body parts demonstrate the effectiveness of the proposed framework.