VisionGo: Towards video retrieval with joint exploration of human and computer

This paper introduces an effective interactive video retrieval system named VisionGo. It jointly explores human and computer to accomplish video retrieval with high effectiveness and efficiency. It assists the interactive video retrieval process in different aspects: (1) it maximizes the interaction efficiency between human and computer by providing a user interface that supports highly effective user annotation and an intuitive visualization of retrieval results; (2) it employs a multiple feedback technique that assists users in choosing proper method to enhance relevance feedback performance; and (3) it facilitates users to assess the retrieval results of motion-related queries by using motion-icons instead of static keyframes. Experimental results based on over 160 h of news video shows demonstrate the effectiveness of the VisionGo system.     

SearchPad: explicit capture of search context to support Web search

Experienced users who query search engines have a complex behavior. They explore many topics in parallel, experiment with query variations, consult multiple search engines, and gather information over many sessions. In the process they need to keep track of search context — namely useful queries and promising result links, which can be hard. We present an extension to search engines called SearchPad that makes it possible to keep track of ‘search context' explicitly. We describe an efficient implementation of this idea deployed on four search engines: AltaVista, Excite, Google and Hotbot. Our design of SearchPad has several desirable properties: (i) portability across all major platforms and browsers; (ii) instant start requiring no code download or special actions on the part of the user; (iii) no server side storage; and (iv) no added client–server communication overhead. An added benefit is that it allows search services to collect valuable relevance information about the results shown to the user. In the context of each query SearchPad can log the actions taken by the user, and in particular record the links that were considered relevant by the user in the context of the query. The service was tested in a multi-platform environment with over 150 users for 4 months and found to be usable and helpful. We discovered that the ability to maintain search context explicitly seems to affect the way people search. Repeat SearchPad users looked at more search results than is typical on the Web, suggesting that availability of search context may partially compensate for non-relevant pages in the ranking.     

Content-based image retrieval with relevance feedback using random walks

In this paper, we propose a novel approach to content-based image retrieval with relevance feedback, which is based on the random walker algorithm introduced in the context of interactive image segmentation. The idea is to treat the relevant and non-relevant images labeled by the user at every feedback round as “seed” nodes for the random walker problem. The ranking score for each unlabeled image is computed as the probability that a random walker starting from that image will reach a relevant seed before encountering a non-relevant one. Our method is easy to implement, parameter-free and scales well to large datasets. Extensive experiments on different real datasets with several image similarity measures show the superiority of our method over different recent approaches.     

互联网社群图像标签排序研究进展

互联网社群图像标签排序是目前计算机视觉、机器学习等领域最热门的课题之一。图像标签序列的合理性直接影响到图像检索等应用的效果。目前图像标签排序的方法多种多样,根据标签排序方法的不同将其划分为基于语义相关度与基于视觉显著性的标签排序,着重介绍了两类方法的典型标签排序方法,分析其各自的优缺点。最后就图像标签排序的评价方法以及发展趋势做了简单的论述。     

基于内容的图像检索技术研究

随着数字图像在多媒体领域的广泛应用,对基于内容的图像检索技术的需求也不断增加.基于内容的图像检索技术总体上可以分为两部分:图像特征提取、图像特征的索引与匹配.图像特征提取主要解决如何在数学上有效地描述一幅图像.文中分别介绍了颜色、形状和纹理特征提取算法近年来的研究成果.图像特征索引与匹配,主要解决如何根据特征描述判断图像间的相似程度,并准确、快速列出图像库中与检索图像相似的图像,分别介绍了相似度测量方法、聚类与分类技术、相关反馈技术三类技术的主要研究成果.最后对基于内容的图像检索技术的研究难点进行了讨论,对未来可能的研究方向进行了展望.     

面向脑电情感识别的改进多分类RVM模型研究

从相关向量机（RVM）和支持向量机（SVM）的相似性以及RVM的稀疏特性出发，将RVM应用于脑电信号（EEG）的情感识别中。针对一对一（OAO）和一对多（OAA）两种多分类方法各自的特点和不足，提出了一种全新的两层多分类模型（OAA-OAO），改进现有OAO算法中无效投票影响最终决策的现象。设计情感EEG信号识别对比实验，验证基于RVM的改进多分类算法在脑电信号情感识别中的应用。对于实验室采集的情感脑电信号，提取其非线性特征（功率谱熵、样本熵和Hurst指数）并采用主成分分析法进行降维。将OAA-OAO-RVM算法分别和OAO-SVM、OAO-RVM两种识别网络进行对比，分析RVM的识别性能以及OAA-OAO多分类算法的分类性能。结果表明，采用降维后的最优特征集合作为识别网络的输入向量得到的识别性能更高，且RVM表现出的性能优于SVM。同时，改进后的OAA-OAO算法较传统OAO模型的平均识别率提高了7.89%，证明OAA-OAO算法可有效去除一部分无效投票从而使分类精度得到显著提高，验证了此模型是一种有效的多分类模型。     

基于概念图的相关反馈技术研究

相关反馈技术是信息检索技术研究的热点。目前常用的反馈技术依然是基于关键词匹配的方式,基于语义的方式缺少概念之间关系的描述。文章提出了一种基于概念图的相关反馈技术,采用概念图的知识表示方式描述概念之间关系,从语义的层次上进行相似度判断,扩展查询式。实验表明该方法可以满足用户的需求,提高检索的效率。     

基于词关联度的语义相关度算法研究

如今网页排名算法很多,基本上可以分为两类:基于超链接和基于内容。比较成熟的算法主要有PageRank、HITS、LSI等。本文基于向量空间模型以及信息论,提出一个与文章内容相关的语义相关度算法模型。该模型将文章语义抽象为词频表,并通过机器学习构建词语之间的关联度表,以此词关联度为基础,计算文章之间的相关度。实验结果表明,文中提出的相关度算法可以有效的根据文章之间语义相关度大小来进行排名。     

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.     

一种新的回归型约简多分辨率相关向量机

提出一种新的稀疏贝叶斯回归算法.基于相关向量机,首先通过尺度核和小波核构造完备基以提高预测精度;然后利用保局投影对输入矩阵的列进行主成分提取以减少训练时间,从而形成算法的初步模型.为进一步减小较大规模训练数据集的回归时间压力,算法对训练数据集的分层采样建立了初步模型,进而产生实际较小规模的训练数据集.实验结果表明,算法在预测精度和鲁棒性上优于传统支持向量机和相关向量机,且其训练时间较相关向量机少.