Image hash code training model algorithm and classification learning method based on binary weight

Abstract

The invention discloses an image hash code training model algorithm and a classification learning method based on binary weights. The model algorithm steps include: selecting a loss function, determining an objective equation, and performing binary encoding on classifiers and training image features; Code for unified learning, update binary code, optimize loss function; derive hash code training model. Also disclosed is a classification learning method using a hash code image training model based on binary weights. The steps include: the image to be searched is obtained through the hash code training model based on binary weights to obtain the hash code, and the hash code and the hash code are obtained. The Hamming distance between the binary codes of the classifiers; find the minimum Hamming distance in the Hamming distance, and obtain the corresponding classifier, which is the category of the image to be searched. The invention can classify images in multiple image categories and high-latitude scenes, improve the performance of algorithms on large-scale data sets, and is accurate, efficient, fast and low in memory consumption.

Description

technical field [0001] The invention belongs to the field of image classification methods, and in particular relates to a binary weight-based image hash code training model and a classification learning method. Background technique [0002] In recent years, due to the explosive growth of the number of digital images and the substantial improvement of image quality, the problem of large-scale visual recognition has attracted great research enthusiasm from academia and industry. The classification problem of images with tens of thousands of categories usually uses conventional classifiers for heavy calculations, such as k-nearest neighbors or k-NN, and support vector machines or SVM. In multi-class image recognition problems, a large number of classifiers produces huge computational and memory overheads, and a large number of classifiers will lead to a surge in complexity during the model training and deployment stages. Imagine that there are C categories, and each category h...

AI Technical Summary

Problems solved by technology

[0010] The purpose of the present invention is to solve the problems of existing image classification algorithms in large-scale image data sets, such as excessive memory usage, high computational overhead, and poor classification results, and provide a method that can be used in various image categories and high-latitude scenes. Under the image classification, improve the performance of the algorithm on large-scale data sets, an image hash code training model algorithm and classification learning method based on binary weights that are accurate, efficient, fast and low memory consumption

Images