{"title":"特征锁定循环及其在图像数据库中的应用","authors":"A. Sherstinsky, Rosalind W. Picard","doi":"10.1109/NNSP.1995.514916","DOIUrl":null,"url":null,"abstract":"We present a new dynamical system called the \"feature-locked loop\". The inputs to this feedback neural network are a set of feature vectors and a one-parameter function that characterizes the data. We show that the feature-locked loop is locally stable for one example of the characteristic function and determines the value of its unknown parameter. We apply this property of the feature-locked loop to the problem of sorting textures by their similarity. We use the feature-locked loop and a priori information to quantify the degree of similarity between the input image and the reported set of image as a whole. The prior knowledge is encoded in the form of the one-parameter function and a general assumption about the number of perceptual outliers in the reported set. The unknown parameter, computed by the feature-locked loop, is then related to the entire set of image features produced by the retrieval.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feature-locked loop and its application to image databases\",\"authors\":\"A. Sherstinsky, Rosalind W. Picard\",\"doi\":\"10.1109/NNSP.1995.514916\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a new dynamical system called the \\\"feature-locked loop\\\". The inputs to this feedback neural network are a set of feature vectors and a one-parameter function that characterizes the data. We show that the feature-locked loop is locally stable for one example of the characteristic function and determines the value of its unknown parameter. We apply this property of the feature-locked loop to the problem of sorting textures by their similarity. We use the feature-locked loop and a priori information to quantify the degree of similarity between the input image and the reported set of image as a whole. The prior knowledge is encoded in the form of the one-parameter function and a general assumption about the number of perceptual outliers in the reported set. The unknown parameter, computed by the feature-locked loop, is then related to the entire set of image features produced by the retrieval.\",\"PeriodicalId\":403144,\"journal\":{\"name\":\"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.1995.514916\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.1995.514916","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feature-locked loop and its application to image databases
We present a new dynamical system called the "feature-locked loop". The inputs to this feedback neural network are a set of feature vectors and a one-parameter function that characterizes the data. We show that the feature-locked loop is locally stable for one example of the characteristic function and determines the value of its unknown parameter. We apply this property of the feature-locked loop to the problem of sorting textures by their similarity. We use the feature-locked loop and a priori information to quantify the degree of similarity between the input image and the reported set of image as a whole. The prior knowledge is encoded in the form of the one-parameter function and a general assumption about the number of perceptual outliers in the reported set. The unknown parameter, computed by the feature-locked loop, is then related to the entire set of image features produced by the retrieval.