基于内容的音频检索系统的前端抗噪技术

基于内容的音频检索具有较高的实用价值，将安静环境下训练的模型应用于实际有背景噪声的环境中。分析各种语音增强技术，并通过性能对比，选取谱减法作为系统前端抗噪技术，给出一个将音频增强和音频检索系统级联组成的抗噪声音频检索系统．并给出适合系统使用的谱减法的改进算法。     

基于内容的视频检索系统的比较和研究

分析了数字视频的特点和优势,阐述了基于内容的视频检索的迫切性和重要性。比较分析了4个典型检索系统,归纳了其系统结构、功能和应用领域等,指出存在的主要问题,并提出了解决方案。分析了研究的热点和方向。     

基于内容的音频检索

音频数据检索是数字化信息检索的迫切需要,传统的基于文本的音频检索技术存在主观性和不完整性等缺点,而且不支持实时音频检索.本文根据音频检索的研究现状,论述了基于内容的音频检索方法,讨论了一些音频检索的关键技术.     

基于内容的图像检索技术

基于内容的图像数据库检索技术是当今的一个研究热点.本文介绍了基于内容图像检索的基本原理、检索方式和关键技术,并列举了几种较为先进的图像检索系统.最后探讨了当前研究中存在的问题以及今后的研究方向.     

基于内容的音频检索关键技术研究

对于音频内容的检索技术被现代运用于许多的领域当中,这项技术的掌握和发展具有很大的应用价值.对于基于内容的音频检索在本文中主要是对其音频中两个分支,语音检索和音乐检索这两个方面进行研究.并且在此基础上通过基于内容的音频检索相关的技术进行相应的认识和具体内容的了解和分析,以此了解和促进音频检索关键技术的运用与发展.     

基于内容的视频检索技术浅析

基于内容的视频检索是当前国内外研究的热点问题,文中介绍了一般视频检索系统的结构,分析了视频检索中的几项关键技术和评价标准,最后指出了当前视频检索算法存在的问题以及今后的发展方向。     

浅谈DIP在视频点播技术中的应用——基于内容的颜色特征图像检索系统

以基于内容的颜色特征图像检索实例系统为基础,研究RGB与HSI颜色空间内,全局直方图,累积直方图,局部累加直方图对图像的描述。以之为特征,再引入欧氏距离的相似性度量方法实现基于内容的图像检索。并成功实现了在数字计算机上的基于内容的颜色特征图像检索系统,并提出该技术应用于VOD的构想。     

基于内容的视频检索技术研究

研究了视频数据库的基于内容检索技术,对其中的关键技术和各种算法进行了描述及评价,并对其存在的问题和研究方向进行了讨论。     

用CORBA技术构建分布式图像检索系统

首先针对现有Web图像搜索引擎设计的局限性,提出了分布式系统集成的重要性.然后,对CORBA技术进行了详细的描述,说明了CORBA技术是构建分布式图像检索系统的理想平台,并且介绍了图像检索技术.最后,提出了一种基于CORBA技术的分布式图像检索系统设计模型,并详细描述了其体系结构.     

基于内容的数字音频快速检索技术综述

本文通过基于内容的数字音频快速检索专利技术申请文献的检索、统计和分析,依据音频检索流程分析了该技术领域的发展分支,特别针对特征提取、音频分割等技术进行阐述。     

Generic content-based audio indexing and retrieval framework

Rapid increase in the amount of the digital audio collections presenting various formats, types, durations and other parameters that the digital multimedia world refers demands a generic framework for robust and efficient indexing and retrieval based on the aural content. Moreover, from the content-based multimedia retrieval point of view, the audio information can be even more important than the visual part as it is mostly unique and significantly stable within the entire duration of the content. A generic and robust audio-based multimedia indexing and retrieval framework, which has been developed and tested under the MUVIS system, is presented. This framework supports the dynamic integration of the audio feature extraction modules during the indexing and retrieval phases and therefore provides a test-bed platform for developing robust and efficient aural feature extraction techniques. Furthermore, the proposed framework is designed based on the high-level content classification and segmentation in order to improve the speed and accuracy of the aural retrievals. Both theoretical and experimental results are finally presented, including the comparative measures of retrieval performance with respect to the visual counterpart.     

Feature selection for content-based image retrieval

In this article, we propose a novel system for feature selection, which is one of the key problems in content-based image indexing and retrieval as well as various other research fields such as pattern classification and genomic data analysis. The proposed system aims at enhancing semantic image retrieval results, decreasing retrieval process complexity, and improving the overall system usability for end-users of multimedia search engines. Three feature selection criteria and a decision method construct the feature selection system. Two novel feature selection criteria based on inner-cluster and intercluster relations are proposed in the article. A majority voting-based method is adapted for efficient selection of features and feature combinations. The performance of the proposed criteria is assessed over a large image database and a number of features, and is compared against competing techniques from the literature. Experiments show that the proposed feature selection system improves semantic performance results in image retrieval systems. This work was supported by the Academy of Finland, Project No. 213,462 (Finnish Centre of Excellence Program 2006–2011).     

Expert content-based image retrieval system using robust local patterns

A new image indexing and retrieval algorithm for content based image retrieval is proposed in this paper. The local region of the image is represented by making the use of local difference operator (LDO), separating it into two components i.e. sign and magnitude. The sign LBP operator (S_LBP) is a generalized LBP operator. The magnitude LBP (M_LBP) operator is calculated using the magnitude of LDO. A robust LBP (RLBP) operator is presented employing robust S_LBP and robust M_LBP. Further, the combination of Gabor transform and RLBP operator has also been presented. The robustness is established by conducting four experiments on different image database i.e. Corel 1000 (DB1), Brodatz texture database (DB2) and MIT VisTex database (DB3) under different lighting (illumination) and noise conditions. Investigations reveal a promising achievement of the technique presented when compared to S_LBP and other existing transform domain techniques in terms of their evaluation measures.     

一种有效的基于内容的图像检索方法

本文针对基于内容的图像检索中特征和相似度问题，提出新的距离模式，并以彩色空间中扩展共发矩阵作为纹理描述，在测试系统iPhoto上，数据库为56600幅图像时，实验结果显示，本文方法优于传统方法。     

Design and analysis of a content-based pathology image retrieval system

A prototype, content-based image retrieval system has been built employing a client/server architecture to access supercomputing power from the physician's desktop. The system retrieves images and their associated annotations from a networked microscopic pathology image database based on content similarity to user supplied query images. Similarity is evaluated based on four image feature types: color histogram, image texture, Fourier coefficients, and wavelet coefficients, using the vector dot product as a distance metric. Current retrieval accuracy varies across pathological categories depending on the number of available training samples and the effectiveness of the feature set. The distance measure of the search algorithm was validated by agglomerative cluster analysis in light of the medical domain knowledge. Results show a correlation between pathological significance and the image document distance value generated by the computer algorithm. This correlation agrees with observed visual similarity. This validation method has an advantage over traditional statistical evaluation methods when sample size is small and where domain knowledge is important. A multi-dimensional scaling analysis shows a low dimensionality nature of the embedded space for the current test set.     

基于内容的图像检索技术

目前各行业对图像的使用越来越广泛,如何有效、快速地从大规模图像数据库中检索出需要的图像,是目前一个相当重要而又富有挑战性的研究课题.但传统的图像检索技术是基于文本的检索技术,这种方法虽然简单易行,但存在一些致命的缺点,严重影响了对图像信息的有效使用.为了克服传统方法的缺点,提出了基于内容的图像检索技术,该技术能够全面客观地提取图像内容,能有效地获取所需的视觉信息,能使图像数据库中的信息得到有效的管理.     

Hybrid textual-visual relevance learning for content-based image retrieval

Learning effective relevance measures plays a crucial role in improving the performance of content-based image retrieval (CBIR) systems. Despite extensive research efforts for decades, how to discover and incorporate semantic information of images still poses a formidable challenge to real-world CBIR systems. In this paper, we propose a novel hybrid textual-visual relevance learning method, which mines textual relevance from image tags and combines textual relevance and visual relevance for CBIR. To alleviate the sparsity and unreliability of tags, we first perform tag completion to fill the missing tags as well as correct noisy tags of images. Then, we capture users’ semantic cognition to images by representing each image as a probability distribution over the permutations of tags. Finally, instead of early fusion, a ranking aggregation strategy is adopted to sew up textual relevance and visual relevance seamlessly. Extensive experiments on two benchmark datasets well verified the promise of our approach.     

Frequency layered color indexing for content-based image retrieval

Image patches of different spatial frequencies are likely to have different perceptual significance as well as reflect different physical properties. Incorporating such concept is helpful to the development of more effective image retrieval techniques. We introduce a method which separates an image into layers, each of which retains only pixels in areas with similar spatial frequency characteristics and uses simple low-level features to index the layers individually. The scheme associates indexing features with perceptual and physical significance thus implicitly incorporating high level knowledge into low level features. We present a computationally efficient implementation of the method, which enhances the power and at the same time retains the simplicity and elegance of basic color indexing. Experimental results are presented to demonstrate the effectiveness of the method.