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

Abstract

The invention discloses a Hash code image training model based on binary weight, and a model algorithm comprises the steps: selecting a loss function, determining a target equation, and performing binary coding of a classifier and training image features; performing unified learning of a binary code, updating the binary code, and optimizing the loss function; and deducing the Hash code training model. The invention also discloses a classification learning method employing the Hash code image training model based on binary weight, and the method comprises the steps: obtaining a Hash code of a to-be-searched image through the Hash code training model based on binary weight, and solving Hamming distances between the Hash code and a classifier binary code; searching in the minimum Hamming distance from the Hamming distances, and obtaining the classifier corresponding to the minimum Hamming distance, wherein the classifier is the category to which the to-be-searched image belongs. The method can be used for the image classification for various types of images in high-latitude scenes, improves the performance of an algorithm in a large-scale data set, is precise, efficient and quick, andis small in consumption of the memory.

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 and classification learning method based on binary weights that are accurate, efficient, fast and low memory consumption

Images