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
The problem considered in this paper is how to detect the degree of similarity in the content of digital images useful in image retrieval,i.e.,to what extent is the content of a query image similar to content of other...The problem considered in this paper is how to detect the degree of similarity in the content of digital images useful in image retrieval,i.e.,to what extent is the content of a query image similar to content of other images.The solution to this problem results from the detection of subsets that are rough sets contained in covers of digital images determined by perceptual tolerance relations(PTRs).Such relations are defined within the context of perceptual representative spaces that hearken back to work by J.H.Poincare on representative spaces as models of physical continua.Classes determined by a PTR provide content useful in content-based image retrieval(CBIR).In addition,tolerance classes provide a means of determining when subsets of image covers are tolerance rough sets(TRSs).It is the nearness of TRSs present in image tolerance spaces that provide a promising approach to CBIR,especially in cases such as satellite images or aircraft identification where there are subtle differences between pairs of digital images,making it difficult to quantify the similarities between such images.The contribution of this article is the introduction of the nearness of tolerance rough sets as an effective means of measuring digital image similarities and,as a significant consequence,successfully carrying out CBIR.展开更多
可截取签名允许签名人根据需要,在不与原始签名人交互的情况下删除已签名中的敏感数据块,并为截取后的数据计算一个公开并且可验证的签名.目前大多数可截取签名方案都是基于传统数论的困难假设构造的,鉴于量子计算机可能构成的威胁,构...可截取签名允许签名人根据需要,在不与原始签名人交互的情况下删除已签名中的敏感数据块,并为截取后的数据计算一个公开并且可验证的签名.目前大多数可截取签名方案都是基于传统数论的困难假设构造的,鉴于量子计算机可能构成的威胁,构造能够抵抗量子计算攻击的可截取签名方案尤为重要.因此基于格的Ring-SIS(ring short integer solution)问题,提出一种理想格上基于身份的可截取签名方案,证明了该方案在选择身份和消息攻击下存在不可伪造性和隐私性.理论分析和效率分析表明,相较于同类方案,该方案在功能性上同时具备身份认证、隐私性和抗量子攻击等多种功能,用户公钥尺寸更短、安全性更高、算法耗时更低.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 the Natural Sciences and Engineering Research Council of Canada (NSERC) research grants 194376 and 185986Manitoba Centre of Excellence Fund(MCEF) grant and Canadian Network Centre of Excellence(NCE) and Canadian Arthritis Network(CAN) grant SRI-BIO-05.
文摘The problem considered in this paper is how to detect the degree of similarity in the content of digital images useful in image retrieval,i.e.,to what extent is the content of a query image similar to content of other images.The solution to this problem results from the detection of subsets that are rough sets contained in covers of digital images determined by perceptual tolerance relations(PTRs).Such relations are defined within the context of perceptual representative spaces that hearken back to work by J.H.Poincare on representative spaces as models of physical continua.Classes determined by a PTR provide content useful in content-based image retrieval(CBIR).In addition,tolerance classes provide a means of determining when subsets of image covers are tolerance rough sets(TRSs).It is the nearness of TRSs present in image tolerance spaces that provide a promising approach to CBIR,especially in cases such as satellite images or aircraft identification where there are subtle differences between pairs of digital images,making it difficult to quantify the similarities between such images.The contribution of this article is the introduction of the nearness of tolerance rough sets as an effective means of measuring digital image similarities and,as a significant consequence,successfully carrying out CBIR.
文摘可截取签名允许签名人根据需要,在不与原始签名人交互的情况下删除已签名中的敏感数据块,并为截取后的数据计算一个公开并且可验证的签名.目前大多数可截取签名方案都是基于传统数论的困难假设构造的,鉴于量子计算机可能构成的威胁,构造能够抵抗量子计算攻击的可截取签名方案尤为重要.因此基于格的Ring-SIS(ring short integer solution)问题,提出一种理想格上基于身份的可截取签名方案,证明了该方案在选择身份和消息攻击下存在不可伪造性和隐私性.理论分析和效率分析表明,相较于同类方案,该方案在功能性上同时具备身份认证、隐私性和抗量子攻击等多种功能,用户公钥尺寸更短、安全性更高、算法耗时更低.
