Image multi-tag marking algorithm based on multi-example package feature learning

Abstract

The invention discloses an image multi-tag marking algorithm based on multi-example package feature learning, and the algorithm comprises the steps: obtaining a set of image blocks of all training images; extracting the features of a color histogram and the features of a direction gradient histogram of each image block of the set of the training images; enabling one training image to serve as an image package, and obtaining an image package structure needed by a multi-example learning framework; enabling the examples in all image packages in the set to form a projection example set, enabling each image package to be projected towards the projection example set, and obtaining the projection features of the image packages; selecting the features with the high discrimination performance as the classification features of the image packages; importing the classification features of the image packages of the learned training image set into an SVM classifier for training, obtaining the parameters of a training model, and predicting a test image tag through employing a trained SVM classifier. The algorithm is simple in implementation, and a trainer is mature and reliable. The algorithm is quick in prediction, and achieves multiple image tags better.

Description

technical field [0001] The invention relates to the fields of multimedia content understanding and computer network content retrieval, in particular to an image multi-label labeling algorithm based on multi-instance package feature learning. Background technique [0002] With the rapid development of computer technology, communication technology and multimedia technology, the Internet has become a vast source of massive multimedia information. The technical means to effectively organize, manage and find these visual information. Content-based image retrieval has become an important research topic. [0003] Traditional supervised learning assumes that an image is represented as an example and labeled with a label. Although these methods have been successfully applied, with the increase of network image types and semantic complexity, the single-instance single-label image annotation method can no longer meet the actual needs, so the multi-instance and multi-label method has ...

AI Technical Summary

Problems solved by technology

Although these methods have been successfully applied, with the increase of network image types and semantic complexity, the single-instance single-label image annotation method can no longer meet the actual needs, so the multi-instance and multi-label method has begun to be applied to image multi-label in label annotation

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