Self-adaptive robust constraint maximum variation mapping (CMVM) feature dimension reduction method for image retrieval of plant laminae

Abstract

The invention discloses a self-adaptive robust constraint maximum variation mapping (CMVM) feature dimension reduction method for image retrieval of plant laminae. According to the self-adaptive robust CMVM feature dimension reduction method, study is performed from the feature extraction and selective levels of an image manifold, and the capacity of keeping the distinguishability of positive type local 'sub-concepts' and the capacity of enhancing the distinguishability of 'concepts' of positive and negative types are realized by a CMVM semi-supervised manifold dimension reduction method to provide effective service for the diversified image retrieval; according to the practical application of the image retrieval, and aiming at basic problems of CMVM, the invention provides a method for removing noise points; the problem of learning of sample exterior points of CMVM is solved by a linear approximation method; and the selection of CMVM manifold parameters and the estimation of the intrinsic dimension of an image are performed by designing an 'ordered' level maximum interval relevance evaluation function of the diversified retrieval, so that a self-adaptive robust CMVM algorithm for the diversified time retrieval is provided. By the self-adaptive robust CMVM feature dimension reduction method, redundant characteristics are removed, and the retrieval efficiency is improved.

Description

technical field [0001] The invention relates to a feature dimension reduction method, in particular to an adaptive robust CMVM feature dimension reduction method for plant leaf image retrieval. Background technique [0002] Plants are one of the life forms with the largest number of species and the widest distribution on the earth. They maintain the balance of carbon dioxide and oxygen in the atmosphere through photosynthesis. At the same time, plants are an important source of food for human beings, as well as resources necessary for human production and life. In addition, plants also play a vital role in soil and water conservation, desert suppression and climate improvement. According to statistics, there are about 400,000 species of plants on earth, of which about 270,000 have been named and recorded by botanists. There are more than 35,000 species of higher plants in my country alone, accounting for about 10.5% of the world's total, and it is the second largest plant ...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to propose an adaptive robust CMVM feature dimensionality reduction method for image retrieval of plant leaves. The method starts from the image manifold feature extraction and selection level, and aims at diverse image retrieval problems. (1) propose Robust CMVM manifold algorithm to solve the noise problem in image data; (2) Propose a CMVM manifold sample outlier learning method and intrinsic dimension estimation method based on linear approximation; (3) Propose a method based on "ordered" hierarchy Adaptive CMVM Manifold Parameters Selection and Intrinsic Dimension Estimation Method for Maximum Margin Correlation Static Evaluation Index

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