Support Vector Machine active learning for 3D model retrieval

In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user＇s semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of relevance feedback.

An Improved Asymmetric Bagging Relevance Feedback Strategy for Medical Image Retrieval

Much attention has been paid to relevant feedback in intelligent computation for social computing, especially in content-based image retrieval which based on WeChat platform for the medical auxiliary. It has a good effect on reducing the semantic gap between high semantics and low semantics of images. There are many kinds of support vector machines (SVM) based relevance feedback methods in image retrieval, but all of them may encounter some problems, such as a small size of sample, an asymmetric positive sample and negative sample as well as a long feedback cycle. To deal with these problems, an improved asymmetric bagging (IAB) relevance feedback algorithm is proposed. Furthermore, we apply a new fuzzy support machine (FSVM) to cooperate with IAB. To solve the over-fitting and real-time problems, we use modified local binary patterns (MLBP) as image features. Finally, experimental results demonstrate that our method performs other methods in terms of improving retrieval precision as well as retrieval efficiency.

Active learning based on maximizing information gain for content-based image retrieval

This paper describes a new method for active learning in content-based image retrieval. The proposed method firstly uses support vector machine (SVM) classifiers to learn an initial query concept. Then the proposed active learning scheme employs similarity measure to check the current version space and selects images with maximum expected information gain to solicit user's label. Finally, the learned query is refined based on the user's further feedback. With the combination of SVM classifier and similarity measure, the proposed method can alleviate model bias existing in each of them. Our experiments on several query concepts show that the proposed method can learn the user's query concept quickly and effectively only with several iterations.

An improved SVM model for relevance feedback in remote sensing image retrieval

With the rapid development of satellite remote sensing technology and an ever-increasing number of Earth observation satellites being launched,the global volume of remotely sensed imagery has been growing exponentially.Processing the variety of remotely sensed data has increasingly been complex and difficult.It is also hard to efficiently and intelligently retrieve what users need from a massive database of images.This paper introduces an improved support vector machine(SVM)model,which optimizes the model parameters and selects the feature subset based on the particle swarm optimization(PSO)method and genetic algorithm(GA)for remote sensing image retrieval.The results from an image retrieval experiment show that our method outperforms traditional methods such as GRID,PSO,and GA in terms of consistency and stability.

Web Information Retrieval： Problem and Prospects

The information access is the rich data available for information retrieval, evolved to provide principle approaches or strategies for searching. For building the successful web retrieval search engine model, there are a number of prospects that arise at the different levels where techniques, such as Usenet, support vector machine are employed to have a significant impact. The present investigations explore the number of problems identified its level and related to finding information on web. The authors have attempted to examine the issues and prospects by applying different methods such as web graph analysis, the retrieval and analysis of newsgroup postings and statistical methods for inferring meaning in text. The proposed model thus assists the users in finding the existing formation of data they need. The study proposes three heuristics model to characterize the balancing between query and feedback information, so that adaptive relevance feedback. The authors have made an attempt to discuss the parameter factors that are responsible for the efficient searching. The important parameters can be taken care of for the future extension or development of search engines.

图像检索中语义映射方法综述

"语义鸿沟"已成为基于内容图像检索的瓶颈,解决这个问题需要建立从图像的低层特征到高层语义的映射.对当前语义映射研究进行了综述,首先给出一个结合语义的图像检索框架,并分析了图像内容的层次模型及图像语义的表示方法;然后根据算法的特点,将现有的语义映射方法和技术分为4大类,重点阐述了各类方法提出的思路、模型,并讨论各自的优势和局限性;最后以图像检索实际应用的需要为依据,提出在图像语义检索相关领域的重要课题和研究方向.

用兴趣点凸包和SVM加权反馈实现图像检索

针对采用环状颜色直方图的图像检索方法存在的不足,提出一种基于兴趣点凸包的图像特征提取方法,通过对用小波变换检测出的兴趣点递归求出它们的凸包,并将每个凸包上的兴趣点按一定的算法安插在相应的桶内,对每个桶求出颜色直方图,利用桶与桶之间的相似度定义两幅图像的相似度.这种特征提取方法可有效抑制兴趣点集合中出现游离兴趣点的情况,结合基于兴趣点的空间离散度和Gabor小波纹理等特征实现图像检索,可有效提高图像检索精度.最后,提出一种新的相关反馈方法,通过利用支持向量机分类结果设置权值来改进移动查询点相关反馈方法.实际图像数据库上的实验表明,引入这种反馈方法后可将图像检索的查准率提高20%左右,查全率提高10%左右.

一种基于SVM和主动学习的图像检索方法

主动学习已被证明是提升基于内容图像检索性能的一种重要技术。而相关反馈技术可以有效地减少用户标注。提出一种主动学习算法,带权Co-ASVM,用于改进相关反馈中样本选择的性能。颜色和纹理可以认为是一张图片的两个充分不相关的视图,分别计算颜色和纹理两种特征空间的权值,并在两种特征空间上分别进行SVM学习,对未标注样本进行分类;为了减少反馈样本的冗余,提出一种K-means聚类的主动反馈策略,将未标注样本返回给用户标注。实验表明,该图像检索方法有较高的准确性,并且有不错的检索效果。

基于本体的图像检索

提出一种基于本体的图像检索方法,该方法首先采用改进的K均值无监督分割方法将图像分割成区域,然后提取每个区域的颜色、形状、位置、纹理等低层描述特征,应用这些特征定义一个简单的对象本体。为了提高图像检索的准确度,最后采用支持向量机(SVM)的相关反馈算法。实验结果表明,提出的方法不仅可以提高检索效率,而且对于缩小低层视觉特征和高层语义特征之间的“语义鸿沟”具有很大的意义。

基于混合学习框架的SVM反馈算法研究

基于支持向量机(Support Vector Machine,SVM)理论的相关反馈技术是可有效提高图像检索性能的重要手段之一.然而,大多数SVM反馈算法普遍受到小样本问题的制约.本文综合了集成学习、半监督学习和主动学习三种方法的技术特点,提出一种混合学习框架下的SVM反馈算法.该算法在Boosting迭代过程中使用了未标记图像,以增加个体SVM之间的差异,从而获得高效的集成学习模型.同时,高效的集成学习模型更有利于寻找富有信息(most-informative)图像,从而也提高了用户主动反馈的效率.实验结果及对比分析表明,混合学习策略可有效改进相关反馈的性能.

综合视觉特征度量与SVM的遥感图像检索方法

基于支持向量机(SVM)的相关反馈算法在反馈过程中只利用SVM的分类器,反馈结果排序会出现一定错误,提出一种改进的相关反馈策略,将图像的视觉特征度量函数和SVM分类器函数进行线性加权,作为相关反馈中的相似性度量准则.实验表明,改进策略能够优化遥感图像检索排序结果,提高检索的精度.

一种基于SVM的视频检索反馈算法

针对于正负例集合不平衡及高维空间运算复杂的问题,文中给出了一种基于SVM的视频检索反馈算法,该算法平衡了正例集合与负例集合数量,抽取部分维度集合训练以节省运算时间。实验结果表明,引入相关反馈,视频检索的检准率和查全率都有所提高,明显提高了检索的效率。

基于遗传算法和SVM的遥感图像检索

目前在基于内容的遥感图像检索中,存在图像内容描述不精确,图像查询精度低以及图像查询结果排序错误的问题.就此问题,提出了一种基于遗传算法优化和支持向量机(SVM)的遥感图像检索方法.该方法首先利用遗传算法优化遥感图像特征组合参数,得到对图像内容描述精确度较高的遥感图像检索模型,然后结合优化后的图像相似度计算模型对传统的SVM相关反馈算法进行改进,将遥感图像的视觉特征信息考虑到相关反馈的图像相似度计算中,并根据用户的反馈信息对图像的相似度计算模型进行修正,从而提高遥感图像查询的精度.实验结果表明,该方法优化了图像查询的排序结果,提高了遥感图像的检索精度,有效地减少了图像检索的反馈次数.

基于模糊多类SVM的图像检索相关反馈

针对反馈固有的正负样本不对称问题和小样本问题,提出一种基于修正模糊多类SVM(FSVMs)的图像检索相关反馈算法.该算法首先将相关反馈看成一个正样本类和多个负样本类间的多分类问题,并针对原始FSVMs中模糊隶属度存在负值的情况进行了修正;然后,将受限随机选择扩展为多类情况来扩充多类负样本,并以记忆标注的方式降低用户多类标注的疲劳和误差.实验结果表明,该方法能在较少的反馈次数内得到较满意的检索结果.

一种基于区域综合特征的图像检索方法

提出一种基于目标区域的图像检索方法,首先采用颜色聚类的分割方法将图像分割成不同的区域,提取每个区域的颜色、位置、形状等低层特征,然后提出一种相似度计算方法实现图像的相似性度量。为了提高图像检索的准确度,最后采用支持向量机(SVM)的相关反馈算法。实验结果表明,基于目标区域的图像检索效果比基于全局图像特征的检索效果有较好的改善。

面向草图检索的相关反馈方法

引入基于有偏SVM的学习机制,提出了一种面向草图检索的相关反馈方法·该方法以草图的全局和结构内容表示与匹配为基础,采用基于有偏SVM学习机制实现相关反馈,可有效地捕捉用户的查询兴趣,改善检索性能·最后通过实例验证了该方法的有效性·

基于SVM的相关反馈图像检索研究

针对全局特征的图像检索不能很好地满足用户的意图和基于图像分割的检索过分依赖复杂的图像分割算法二者的不足,在基于子图的图像检索思想的基础上,给出了一种基于用户感兴趣区域的图像检索算法,并通过基于SVM的相关反馈算法来更好地捕获用户的检索意图。实验证明,该算法能发挥用户在检索过程中的作用,具有简单、高效、查全率较高的优点。

一种基于内容图像检索的半监督和主动学习算法

为了提高图像检索中相关反馈算法的效率,提出了一种新的基于相关概率的主动学习算法SVMpr,并结合半监督学习,设计了基于半监督的主动学习图像检索框架。在相关反馈过程中,首先利用半监督学习算法TSVM对标记样本进行训练,然后根据提出的主动学习算法从未标记图像中选取k幅有利于优化学习过程的图像并反馈给用户标记。与传统的相关反馈算法相比,该文提出的图像检索框架显著提高了学习器的效率和性能,并快速收敛于用户的查询概念。

基于内容的图像检索中SVM和Boosting方法集成应用

提出一种适用于图像内容检索的AdaBoostSVM算法。算法思想是采用支持向量机(SVM)作为AdaBoost算法的分量分类器;基于相关反馈检索机制,通过增加重要样本来模拟AdaBoost算法的权重调整方法。在包含2000幅图像的数据库中进行了检索实验,结果表明AdaBoostSVM算法能有效提高系统的检索性能。

一种主动感知的纹理图像检索方法(英文)

基于Contourlet变换和支持向量机提出了一种新的纹理图像检索方法。在这种方法中,能量和广义高斯分布参数被用做Contourlet子带图像的特征。通过这种表示,提出了由一类和二类支持向量机组成的两阶段检索算法来完成感知相似性测度。通过具有640个纹理图像的VisTex库和具有1760个纹理图像的Brodatz库证明了所提方法的有效性。实验结果表明,对于这两个纹理库,新的纹理图像检索方法的平均检索率分别达99.38%和98.07%。