特征锁定循环及其在图像数据库中的应用

A. Sherstinsky, Rosalind W. Picard
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引用次数: 0

摘要

我们提出了一种新的动力系统,称为“特征锁定环”。该反馈神经网络的输入是一组特征向量和表征数据的单参数函数。对于特征函数的一个例子,我们证明了特征锁环是局部稳定的,并确定了其未知参数的值。我们将特征锁定循环的这一特性应用于根据相似性对纹理进行排序的问题。我们使用特征锁定循环和先验信息来量化输入图像与报告图像集之间的相似程度。先验知识以单参数函数和关于报告集合中感知异常值数量的一般假设的形式编码。未知参数由特征锁定循环计算,然后与检索产生的整个图像特征集相关联。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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