Hybrid Gauss model matching method based on improved soil moving distance

Abstract

The invention provides an algorithm system applicable to image classification and image retrieval. The algorithm mainly comprises image modeling and and image matching modules. During image modeling, for improving characteristic description capability, a deep convolution neural network is utilized to extract depth characteristics of an image for the first time, and the hybrid Gauss model is utilized for modeling to represent the image on the basis; during image matching, for two problems of efficiency of a soil moving distance algorithm and noise sensitivity, the accurate, efficient and improved soil moving distance algorithm is proposed, Gauss noise and sparse constraints are additionally considered in an algorithm design process, moreover, through the improved designed soil moving distance algorithm, design defects of a geodesic distance measurement algorithm in the prior art are solved, after a Riemannian geometric structure of Gauss distribution is considered, three types of geodesic distance measurement modes are designed, Gauss distribution distances can be accurately measured.

Description

technical field [0001] The present invention relates to the technical fields of computer vision, probability statistics, and Riemannian geometry. It specifically utilizes deep convolutional neural network features to model mixed Gaussian models, and proposes an efficient and robust measure for the similarity measurement between mixed Gaussian models. algorithm. Background technique [0002] In the process of image processing, image feature representation, as the first step of image processing, plays a very important role. The feature representation of the image mainly expresses the image as one or more matrices in the mathematical sense through a series of algorithms. Image feature representation methods at this stage are mainly divided into two categories: features based on handcrafted features and features based on deep convolutional neural networks. The former is simple and efficient to calculate, including Scale-invariant feature transform (SIFT), Histogram of Oriented...

AI Technical Summary

Problems solved by technology

However, the traditional EMD algorithm still has the problems of computational efficiency and noise sensitivity. This method proposes an improved bulldozing distance algorithm (The Improved-Earth Mover’s Distance-I-EMD) to address these shortcomings.

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