Topic model-fused scene image classification method

Abstract

The invention discloses a topic model-fused scene image classification method, and relates to the field of deep learning and image classification. The method comprises the following steps of: preprocessing data sets, and expanding the quantity of obtained data sets so as to obtain an image data format according with deep learning model processing; constructing a convolutional neural network modelaccording with scene image classification, and pre-training the processed image data sets by using a convolutional neural network; and carrying out end-to-end iterative training on the constructed convolutional neural network by using training sets, adjusting parameters in the network, verifying the trained model by using verification sets, modeling extracted scene image features with discrimination ability, extracting hidden topic variables between the features and images so as to obtain image topic distribution represented by a k-dimensional vector, wherein k represents a topic quantity, each image can be considered as a probability distribution diagram formed by a plurality of topics, and scene image classification is realized by utilizing a classifier.

Description

technical field [0001] The invention belongs to the technical field of deep learning and image classification and recognition, and specifically relates to a method for classifying scene images by integrating theme models. Background technique [0002] Scene image classification, that is, given a set of scene images containing multiple target categories (such as mountains, rivers, roads, etc.), the global semantics of the image is analyzed and understood according to the distribution relationship of each target category. Scene image classification not only has an overall understanding of the category of the entire image, but also analyzes the context information between objects and regions in the image, which enables a deeper understanding of the content of the image and promotes machine vision. The development of fields such as object recognition and image retrieval has a wide range of applications. [0003] With the rapid development of intelligent camera equipment and com...

AI Technical Summary

Problems solved by technology

[0005] The present invention aims to solve the problem of the semantic gap between the bottom layer and the high layer caused by the inconsistency between the bottom layer visual information of the image and the high layer semantic information that people understand the image in the prior art

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