<div style="text-align:justify;"> Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient to...<div style="text-align:justify;"> Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and color similarity. Retrieving images based on the content which is color, texture, and shape is called content based image retrieval (CBIR). The content is actually the feature of an image and these features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features. There are two kinds of content based image retrieval which are general image retrieval and application specific image retrieval. For the general image retrieval, the goal of the query is to obtain images with the same object as the query. Such CBIR imitates web search engines for images rather than for text. For application specific, the purpose tries to match a query image to a collection of images of a specific type such as fingerprints image and x-ray. In this paper, the general architecture, various functional components, and techniques of CBIR system are discussed. CBIR techniques discussed in this paper are categorized as CBIR using color, CBIR using texture, and CBIR using shape features. This paper also describe about the comparison study about color features, texture features, shape features, and combined features (hybrid techniques) in terms of several parameters. The parameters are precision, recall and response time. </div>展开更多
In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts...In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts. To facilitate the decision-making in the health-care and the related areas, in this paper, a two-step content-based medical image retrieval algorithm is proposed. Firstly, in the preprocessing step, the image segmentation is performed to distinguish image objects, and on the basis of the ...展开更多
Cloth image retrieval in E-Commerce is a challenging task. In this paper, we propose an effective approach to solve this problem. Our work chooses three features for retrieval: (1) description (2) category (3) color f...Cloth image retrieval in E-Commerce is a challenging task. In this paper, we propose an effective approach to solve this problem. Our work chooses three features for retrieval: (1) description (2) category (3) color features. It can handle clothes with multiple colors, complex background, and model disturbances. To evaluate the proposed method, we collect a set of women cloth images from Amazon.com. Results reported here demonstrate the robustness and effectiveness of our retrieval method.展开更多
近20年来,音频压缩技术的成熟及互联网的普及使得音乐迅速从磁带和激光唱盘(CD)转变为互联网上以MP3为代表的数字音乐.海量数字音乐带来分类组织、查询检索、内容理解与分析等一系列问题,促使产生了一个新兴的交叉学科,即基于内容的音...近20年来,音频压缩技术的成熟及互联网的普及使得音乐迅速从磁带和激光唱盘(CD)转变为互联网上以MP3为代表的数字音乐.海量数字音乐带来分类组织、查询检索、内容理解与分析等一系列问题,促使产生了一个新兴的交叉学科,即基于内容的音乐信息检索(Content-based Music Information Retrieval,MIR).本文阐述了MIR与音乐科技、声音与音乐计算、计算机听觉、语音信息处理、音乐声学等各个相关领域概念的区别与联系,将MIR技术的数十个研究领域按照与音乐要素的密切程度划分为核心层与应用层.分类总结了各领域的概念、原理、应用、基本技术框架及典型文献,同时介绍了研究中常用的音乐领域知识并明确了中英文术语.最后总结MIR领域存在的各方面问题,并展望其未来发展趋势.展开更多
分析基于内容的音乐信息检索(music information retrieval,MIR),其关键在于特征提取.传统的单特征向量表示方法存在局限性:难以选定用于提取特征的片段或时间窗;只选取音乐片段会丢失一些重要的信息.为了消除局限性,引入多特征向量的...分析基于内容的音乐信息检索(music information retrieval,MIR),其关键在于特征提取.传统的单特征向量表示方法存在局限性:难以选定用于提取特征的片段或时间窗;只选取音乐片段会丢失一些重要的信息.为了消除局限性,引入多特征向量的特征表示方法,在获取音乐的多个声学特征向量的同时,也可以完整地表示该音乐曲目.为了更加准确地计算由多特征向量表示的2个音乐曲目之间的相似度,引入金字塔匹配核技术(pyramid match kernel,PMK)计算不同长度的多特征向量之间的相似度.实验结果表明,PMK技术的引入可以提高MIR的性能.展开更多
文摘<div style="text-align:justify;"> Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and color similarity. Retrieving images based on the content which is color, texture, and shape is called content based image retrieval (CBIR). The content is actually the feature of an image and these features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features. There are two kinds of content based image retrieval which are general image retrieval and application specific image retrieval. For the general image retrieval, the goal of the query is to obtain images with the same object as the query. Such CBIR imitates web search engines for images rather than for text. For application specific, the purpose tries to match a query image to a collection of images of a specific type such as fingerprints image and x-ray. In this paper, the general architecture, various functional components, and techniques of CBIR system are discussed. CBIR techniques discussed in this paper are categorized as CBIR using color, CBIR using texture, and CBIR using shape features. This paper also describe about the comparison study about color features, texture features, shape features, and combined features (hybrid techniques) in terms of several parameters. The parameters are precision, recall and response time. </div>
文摘In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts. To facilitate the decision-making in the health-care and the related areas, in this paper, a two-step content-based medical image retrieval algorithm is proposed. Firstly, in the preprocessing step, the image segmentation is performed to distinguish image objects, and on the basis of the ...
文摘Cloth image retrieval in E-Commerce is a challenging task. In this paper, we propose an effective approach to solve this problem. Our work chooses three features for retrieval: (1) description (2) category (3) color features. It can handle clothes with multiple colors, complex background, and model disturbances. To evaluate the proposed method, we collect a set of women cloth images from Amazon.com. Results reported here demonstrate the robustness and effectiveness of our retrieval method.
文摘近20年来,音频压缩技术的成熟及互联网的普及使得音乐迅速从磁带和激光唱盘(CD)转变为互联网上以MP3为代表的数字音乐.海量数字音乐带来分类组织、查询检索、内容理解与分析等一系列问题,促使产生了一个新兴的交叉学科,即基于内容的音乐信息检索(Content-based Music Information Retrieval,MIR).本文阐述了MIR与音乐科技、声音与音乐计算、计算机听觉、语音信息处理、音乐声学等各个相关领域概念的区别与联系,将MIR技术的数十个研究领域按照与音乐要素的密切程度划分为核心层与应用层.分类总结了各领域的概念、原理、应用、基本技术框架及典型文献,同时介绍了研究中常用的音乐领域知识并明确了中英文术语.最后总结MIR领域存在的各方面问题,并展望其未来发展趋势.
文摘分析基于内容的音乐信息检索(music information retrieval,MIR),其关键在于特征提取.传统的单特征向量表示方法存在局限性:难以选定用于提取特征的片段或时间窗;只选取音乐片段会丢失一些重要的信息.为了消除局限性,引入多特征向量的特征表示方法,在获取音乐的多个声学特征向量的同时,也可以完整地表示该音乐曲目.为了更加准确地计算由多特征向量表示的2个音乐曲目之间的相似度,引入金字塔匹配核技术(pyramid match kernel,PMK)计算不同长度的多特征向量之间的相似度.实验结果表明,PMK技术的引入可以提高MIR的性能.
