This paper presents a novel efficient semantic image classification algorithm for high-level feature indexing of high-dimension image database. Experiments show that the algorithm performs well. The size of the train ...This paper presents a novel efficient semantic image classification algorithm for high-level feature indexing of high-dimension image database. Experiments show that the algorithm performs well. The size of the train set and the test set is 7 537 and 5 000 respectively. Based on this theory, another ground is built with 12,000 images, which are divided into three classes: city, landscape and person, the total result of the classifications is 88.92%, meanwhile, some preliminary results are presented for image understanding based on semantic image classification and low level features. The groundtruth for the experiments is built with the images from Corel database, photos and some famous face databases.展开更多
With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scal...With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scale image/video databases is the most important retrieval control target. In this paper, a cloud based content-based image retrieval (CBIR) scheme is presented. Database-categorizing based on weighted-inverted index (DCWII) and database f'dtering algorithm (DFA) is used to speed up the features matching process. In the DCWII, the weights are assigned to discrete cosine transform (DCT) coefficients histograms and the database is categorized by weighted features. In addition, the DFA filters out the irrelevant image in the database to reduce unnecessary computation loading for features matching. Experiments show that the proposed CBIR scheme outperforms previous work in the precision-recall performance and maintains mean average precision (mAP) about 0.678 in the large-scale database comprising one million images. Our scheme also can reduce about 50% to 85% retrieval time by pre-filtering the database, which helps to improve the efficiency of retrieval systems.展开更多
Developments in multimedia technologies have paved way for the storage of huge collections of video doc- uments on computer systems. It is essential to design tools for content-based access to the documents, so as to ...Developments in multimedia technologies have paved way for the storage of huge collections of video doc- uments on computer systems. It is essential to design tools for content-based access to the documents, so as to allow an efficient exploitation of these collections. Content based anal- ysis provides a flexible and powerful way to access video data when compared with the other traditional video analysis tech- niques. The area of content based video indexing and retrieval (CBVIR), focusing on automating the indexing, retrieval and management of video, has attracted extensive research in the last decade. CBVIR is a lively area of research with endur- ing acknowledgments from several domains. Herein a vital assessment of contemporary researches associated with the content-based indexing and retrieval of visual information. In this paper, we present an extensive review of significant researches on CBV1R. Concise description of content based video analysis along with the techniques associated with the content based video indexing and retrieval is presented.展开更多
多媒体数据尤其是图像数据的急剧增长,使得基于图像内容的检索成为一个非常重要的研究课题.图像的特征描述以及特征的索引机制是实现基于内容图像检索的关键.针对图像局部聚合描述符(Vectors of LocallyAggregated Descriptors,VLAD)中...多媒体数据尤其是图像数据的急剧增长,使得基于图像内容的检索成为一个非常重要的研究课题.图像的特征描述以及特征的索引机制是实现基于内容图像检索的关键.针对图像局部聚合描述符(Vectors of LocallyAggregated Descriptors,VLAD)中硬分配难以准确描述局部特征向量与聚类之间隶属关系的问题,采用软分配策略,根据局部特征向量与聚类中心的距离分配不同的隶属权值,生成更具代表性的软分配局部聚合描述符(SoftAssignment-VLAD,SA-VLAD).针对非对称距离计算倒排索引机制(Inverted File with Asymmetric Distance Com-putation,IVFADC)在查询时为保证结果的查全率而增加候选倒排索引链的数目,导致距离计算和查询时间增加的问题,提出引入简单的散分配方法,将可能落入多条链表中的数据库向量进行多次编码,实现了基于散分配的非对称距离计算倒排索引机制(Dispersed Assignment-IVFADC,DA-IVFADC).实验结果表明:DA-IVFADC机制与SA-VLAD描述符,在很大程度上减少了查询时间,同时有效提高了查询结果的准确率.展开更多
文摘This paper presents a novel efficient semantic image classification algorithm for high-level feature indexing of high-dimension image database. Experiments show that the algorithm performs well. The size of the train set and the test set is 7 537 and 5 000 respectively. Based on this theory, another ground is built with 12,000 images, which are divided into three classes: city, landscape and person, the total result of the classifications is 88.92%, meanwhile, some preliminary results are presented for image understanding based on semantic image classification and low level features. The groundtruth for the experiments is built with the images from Corel database, photos and some famous face databases.
基金supported by"MOST"under Grant No.104-2221-E-011-056
文摘With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scale image/video databases is the most important retrieval control target. In this paper, a cloud based content-based image retrieval (CBIR) scheme is presented. Database-categorizing based on weighted-inverted index (DCWII) and database f'dtering algorithm (DFA) is used to speed up the features matching process. In the DCWII, the weights are assigned to discrete cosine transform (DCT) coefficients histograms and the database is categorized by weighted features. In addition, the DFA filters out the irrelevant image in the database to reduce unnecessary computation loading for features matching. Experiments show that the proposed CBIR scheme outperforms previous work in the precision-recall performance and maintains mean average precision (mAP) about 0.678 in the large-scale database comprising one million images. Our scheme also can reduce about 50% to 85% retrieval time by pre-filtering the database, which helps to improve the efficiency of retrieval systems.
文摘Developments in multimedia technologies have paved way for the storage of huge collections of video doc- uments on computer systems. It is essential to design tools for content-based access to the documents, so as to allow an efficient exploitation of these collections. Content based anal- ysis provides a flexible and powerful way to access video data when compared with the other traditional video analysis tech- niques. The area of content based video indexing and retrieval (CBVIR), focusing on automating the indexing, retrieval and management of video, has attracted extensive research in the last decade. CBVIR is a lively area of research with endur- ing acknowledgments from several domains. Herein a vital assessment of contemporary researches associated with the content-based indexing and retrieval of visual information. In this paper, we present an extensive review of significant researches on CBV1R. Concise description of content based video analysis along with the techniques associated with the content based video indexing and retrieval is presented.
文摘多媒体数据尤其是图像数据的急剧增长,使得基于图像内容的检索成为一个非常重要的研究课题.图像的特征描述以及特征的索引机制是实现基于内容图像检索的关键.针对图像局部聚合描述符(Vectors of LocallyAggregated Descriptors,VLAD)中硬分配难以准确描述局部特征向量与聚类之间隶属关系的问题,采用软分配策略,根据局部特征向量与聚类中心的距离分配不同的隶属权值,生成更具代表性的软分配局部聚合描述符(SoftAssignment-VLAD,SA-VLAD).针对非对称距离计算倒排索引机制(Inverted File with Asymmetric Distance Com-putation,IVFADC)在查询时为保证结果的查全率而增加候选倒排索引链的数目,导致距离计算和查询时间增加的问题,提出引入简单的散分配方法,将可能落入多条链表中的数据库向量进行多次编码,实现了基于散分配的非对称距离计算倒排索引机制(Dispersed Assignment-IVFADC,DA-IVFADC).实验结果表明:DA-IVFADC机制与SA-VLAD描述符,在很大程度上减少了查询时间,同时有效提高了查询结果的准确率.