Research on the Method of Feature-Based Multi-scale Vector Data Model
نویسندگان
چکیده
Multi-scale representation of spatial data is a research focus in GIS, while building multi-scale data model is a key to implementing multi-scale representation of vector data. In view of the shortcomings of existing multi-scale data model in geographical cognition and special analysis, this paper puts forward a method of feature-based, and studies on it qualitatively from definition, description and extraction. Compared to conventional single-scale E-R model, in this paper, the key strategies of building multi-scale conceptual model are put forward. Deeply study and analysis are applied on abstraction and expression of multiple geometric characteristics, of multi-attribution, and of semantic relation among different scales,and the design of feature-based multi-scale conceptual model is realized. Finally, the object-oriented multi-scale logic model is researched, which lays a theoretical foundation for building the feature-based multi-scale vector data model.
منابع مشابه
Feature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine
Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods. In filter methods, features subsets are selected due to some measu...
متن کاملSUBCLASS FUZZY-SVM CLASSIFIER AS AN EFFICIENT METHOD TO ENHANCE THE MASS DETECTION IN MAMMOGRAMS
This paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. In this method, mammograms are segmented into regions of interest (ROI) in order to extract features including geometrical and contourlet coefficients. The extracted features benefit from...
متن کاملClassification of transformer faults using frequency response analysis based on cross-correlation technique and support vector machine
One of the most important methods for transformers fault diagnosis (especially mechanical defects) is the frequency response analysis (FRA) method. The most important step in the FRA diagnostic process is to differentiate the faults and classify them in different classes. This paper uses the intelligent support vector machine (SVM) method to classify transformer faults. For this purpose, two gr...
متن کاملIntroducing a method for extracting features from facial images based on applying transformations to features obtained from convolutional neural networks
In pattern recognition, features are denoting some measurable characteristics of an observed phenomenon and feature extraction is the procedure of measuring these characteristics. A set of features can be expressed by a feature vector which is used as the input data of a system. An efficient feature extraction method can improve the performance of a machine learning system such as face recognit...
متن کاملTowards constructing an Integrative, Multi-Level Model for Cognition: The Function of Semantic Networks
Integrated approaches try to connect different constructs in different theories and reinterpret them using a common conceptual framework. In this research, using the concept of processing levels, an integrated, three-level model of the cognitive systems has been proposed and evaluated. Processing levels are divided into three categories of Feature-Oriented, Semantic and Conceptual Level based o...
متن کامل